DeePMD-kit’s documentation

DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems.

Important

The project DeePMD-kit is licensed under GNU LGPLv3.0. If you use this code in any future publications, please cite this using Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. “DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.” Computer Physics Communications 228 (2018): 178-184.

Getting Started

In this text, we will call the deep neural network that is used to represent the interatomic interactions (Deep Potential) the model. The typical procedure of using DeePMD-kit is

Easy install

There are various easy methods to install DeePMD-kit. Choose one that you prefer. If you want to build by yourself, jump to the next two sections.

After your easy installation, DeePMD-kit (dp) and LAMMPS (lmp) will be available to execute. You can try dp -h and lmp -h to see the help. mpirun is also available considering you may want to train models or run LAMMPS in parallel.

Note

Note: The off-line packages and conda packages require the GNU C Library 2.17 or above. The GPU version requires compatible NVIDIA driver to be installed in advance. It is possible to force conda to override detection when installation, but these requirements are still necessary during runtime.

Install off-line packages
Install with conda
Install with docker

Install off-line packages

Both CPU and GPU version offline packages are available in the Releases page.

Some packages are splited into two files due to size limit of GitHub. One may merge them into one after downloading:

cat deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh.0 deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh.1 > deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh

One may enable the environment using

conda activate /path/to/deepmd-kit

Install with conda

DeePMD-kit is avaiable with conda. Install Anaconda or Miniconda first.

One may create an environment that contains the CPU version of DeePMD-kit and LAMMPS:

conda create -n deepmd deepmd-kit=*=*cpu libdeepmd=*=*cpu lammps -c https://conda.deepmodeling.com -c defaults

Or one may want to create a GPU environment containing CUDA Toolkit:

conda create -n deepmd deepmd-kit=*=*gpu libdeepmd=*=*gpu lammps cudatoolkit=11.6 horovod -c https://conda.deepmodeling.com -c defaults

One could change the CUDA Toolkit version from 10.2 or 11.6.

One may speficy the DeePMD-kit version such as 2.1.1 using

conda create -n deepmd deepmd-kit=2.1.1=*cpu libdeepmd=2.1.1=*cpu lammps horovod -c https://conda.deepmodeling.com -c defaults

One may enable the environment using

conda activate deepmd

Install with docker

A docker for installing the DeePMD-kit is available here.

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:2.1.1_cpu

To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:2.1.1_cuda11.6_gpu

To pull the ROCm version:

docker pull deepmodeling/dpmdkit-rocm:dp2.0.3-rocm4.5.2-tf2.6-lmp29Sep2021

Prepare data with dpdata

One can use the a convenient tool dpdata to convert data directly from the output of first principle packages to the DeePMD-kit format.

To install one can execute

pip install dpdata

An example of converting data VASP data in OUTCAR format to DeePMD-kit data can be found at

$deepmd_source_dir/examples/data_conv

Switch to that directory, then one can convert data by using the following python script

import dpdata
dsys = dpdata.LabeledSystem('OUTCAR')
dsys.to('deepmd/npy', 'deepmd_data', set_size = dsys.get_nframes())

get_nframes() method gets the number of frames in the OUTCAR, and the argument set_size enforces that the set size is equal to the number of frames in the system, viz. only one set is created in the system.

The data in DeePMD-kit format is stored in the folder deepmd_data.

A list of all supported data format and more nice features of dpdata can be found at the official website.

Train a model

Several examples of training can be found at the examples directory:

$ cd $deepmd_source_dir/examples/water/se_e2_a/

After switching to that directory, the training can be invoked by

$ dp train input.json

where input.json is the name of the input script.

By default, the verbosity level of the DeePMD-kit is INFO, one may see a lot of important information on the code and environment showing on the screen. Among them two pieces of information regarding data systems worth special notice.

DEEPMD INFO    ---Summary of DataSystem: training     -----------------------------------------------
DEEPMD INFO    found 3 system(s):
DEEPMD INFO                                        system  natoms  bch_sz   n_bch   prob  pbc
DEEPMD INFO                         ../data_water/data_0/     192       1      80  0.250    T
DEEPMD INFO                         ../data_water/data_1/     192       1     160  0.500    T
DEEPMD INFO                         ../data_water/data_2/     192       1      80  0.250    T
DEEPMD INFO    --------------------------------------------------------------------------------------
DEEPMD INFO    ---Summary of DataSystem: validation   -----------------------------------------------
DEEPMD INFO    found 1 system(s):
DEEPMD INFO                                        system  natoms  bch_sz   n_bch   prob  pbc
DEEPMD INFO                          ../data_water/data_3     192       1      80  1.000    T
DEEPMD INFO    --------------------------------------------------------------------------------------

The DeePMD-kit prints detailed information on the training and validation data sets. The data sets are defined by training_data and validation_data defined in the training section of the input script. The training data set is composed by three data systems, while the validation data set is composed by one data system. The number of atoms, batch size, number of batches in the system and the probability of using the system are all shown on the screen. The last column presents if the periodic boundary condition is assumed for the system.

During the training, the error of the model is tested every disp_freq training steps with the batch used to train the model and with numb_btch batches from the validating data. The training error and validation error are printed correspondingly in the file disp_file (default is lcurve.out). The batch size can be set in the input script by the key batch_size in the corresponding sections for training and validation data set. An example of the output

#  step      rmse_val    rmse_trn    rmse_e_val  rmse_e_trn    rmse_f_val  rmse_f_trn         lr
    3.33e+01    3.41e+01      1.03e+01    1.03e+01      8.39e-01    8.72e-01    1.0e-03
    2.57e+01    2.56e+01      1.87e+00    1.88e+00      8.03e-01    8.02e-01    1.0e-03
    2.45e+01    2.56e+01      2.26e-01    2.21e-01      7.73e-01    8.10e-01    1.0e-03
    1.62e+01    1.66e+01      5.01e-02    4.46e-02      5.11e-01    5.26e-01    1.0e-03
    1.36e+01    1.32e+01      1.07e-02    2.07e-03      4.29e-01    4.19e-01    1.0e-03
    1.07e+01    1.05e+01      2.45e-03    4.11e-03      3.38e-01    3.31e-01    1.0e-03

The file contains 8 columns, form left to right, are the training step, the validation loss, training loss, root mean square (RMS) validation error of energy, RMS training error of energy, RMS validation error of force, RMS training error of force and the learning rate. The RMS error (RMSE) of the energy is normalized by number of atoms in the system. One can visualize this file by a simple Python script:

import numpy as np
import matplotlib.pyplot as plt

data = np.genfromtxt("lcurve.out", names=True)
for name in data.dtype.names[1:-1]:
    plt.plot(data['step'], data[name], label=name)
plt.legend()
plt.xlabel('Step')
plt.ylabel('Loss')
plt.xscale('symlog')
plt.yscale('log')
plt.grid()
plt.show()

Checkpoints will be written to files with prefix save_ckpt every save_freq training steps.

Warning

It is warned that the example water data (in folder examples/water/data) is of very limited amount, is provided only for testing purpose, and should not be used to train a production model.

Freeze a model

The trained neural network is extracted from a checkpoint and dumped into a database. This process is called “freezing” a model. The idea and part of our code are from Morgan. To freeze a model, typically one does

$ dp freeze -o graph.pb

in the folder where the model is trained. The output database is called graph.pb.

Test a model

The frozen model can be used in many ways. The most straightforward test can be performed using dp test. A typical usage of dp test is

dp test -m graph.pb -s /path/to/system -n 30

where -m gives the tested model, -s the path to the tested system and -n the number of tested frames. Several other command line options can be passed to dp test, which can be checked with

$ dp test --help

An explanation will be provided

usage: dp test [-h] [-m MODEL] [-s SYSTEM] [-S SET_PREFIX] [-n NUMB_TEST]
               [-r RAND_SEED] [--shuffle-test] [-d DETAIL_FILE]

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Frozen model file to import
  -s SYSTEM, --system SYSTEM
                        The system dir
  -S SET_PREFIX, --set-prefix SET_PREFIX
                        The set prefix
  -n NUMB_TEST, --numb-test NUMB_TEST
                        The number of data for test
  -r RAND_SEED, --rand-seed RAND_SEED
                        The random seed
  --shuffle-test        Shuffle test data
  -d DETAIL_FILE, --detail-file DETAIL_FILE
                        The file containing details of energy force and virial
                        accuracy

Run MD with LAMMPS

Running an MD simulation with LAMMPS is simpler. In the LAMMPS input file, one needs to specify the pair style as follows

pair_style     deepmd graph.pb
pair_coeff     * *

where graph.pb is the file name of the frozen model. It should be noted that LAMMPS counts atom types starting from 1, therefore, all LAMMPS atom type will be firstly subtracted by 1, and then passed into the DeePMD-kit engine to compute the interactions.

Installation

Easy install

There are various easy methods to install DeePMD-kit. Choose one that you prefer. If you want to build by yourself, jump to the next two sections.

After your easy installation, DeePMD-kit (dp) and LAMMPS (lmp) will be available to execute. You can try dp -h and lmp -h to see the help. mpirun is also available considering you may want to train models or run LAMMPS in parallel.

Note

Note: The off-line packages and conda packages require the GNU C Library 2.17 or above. The GPU version requires compatible NVIDIA driver to be installed in advance. It is possible to force conda to override detection when installation, but these requirements are still necessary during runtime.

Install off-line packages
Install with conda
Install with docker

Install off-line packages

Both CPU and GPU version offline packages are available in the Releases page.

Some packages are splited into two files due to size limit of GitHub. One may merge them into one after downloading:

cat deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh.0 deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh.1 > deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh

One may enable the environment using

conda activate /path/to/deepmd-kit

Install with conda

DeePMD-kit is avaiable with conda. Install Anaconda or Miniconda first.

One may create an environment that contains the CPU version of DeePMD-kit and LAMMPS:

conda create -n deepmd deepmd-kit=*=*cpu libdeepmd=*=*cpu lammps -c https://conda.deepmodeling.com -c defaults

Or one may want to create a GPU environment containing CUDA Toolkit:

conda create -n deepmd deepmd-kit=*=*gpu libdeepmd=*=*gpu lammps cudatoolkit=11.6 horovod -c https://conda.deepmodeling.com -c defaults

One could change the CUDA Toolkit version from 10.2 or 11.6.

One may speficy the DeePMD-kit version such as 2.1.1 using

conda create -n deepmd deepmd-kit=2.1.1=*cpu libdeepmd=2.1.1=*cpu lammps horovod -c https://conda.deepmodeling.com -c defaults

One may enable the environment using

conda activate deepmd

Install with docker

A docker for installing the DeePMD-kit is available here.

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:2.1.1_cpu

To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:2.1.1_cuda11.6_gpu

To pull the ROCm version:

docker pull deepmodeling/dpmdkit-rocm:dp2.0.3-rocm4.5.2-tf2.6-lmp29Sep2021

Install from source code

Please follow our github webpage to download the latest released version and development version.

Or get the DeePMD-kit source code by git clone

cd /some/workspace
git clone --recursive https://github.com/deepmodeling/deepmd-kit.git deepmd-kit

The --recursive option clones all submodules needed by DeePMD-kit.

For convenience, you may want to record the location of source to a variable, saying deepmd_source_dir by

cd deepmd-kit
deepmd_source_dir=`pwd`

Install the python interface

Install the Tensorflow’s python interface

First, check the python version on your machine

python --version

We follow the virtual environment approach to install TensorFlow’s Python interface. The full instruction can be found on the official TensorFlow website. TensorFlow 1.8 or later is supported. Now we assume that the Python interface will be installed to virtual environment directory $tensorflow_venv

virtualenv -p python3 $tensorflow_venv
source $tensorflow_venv/bin/activate
pip install --upgrade pip
pip install --upgrade tensorflow

It is important that everytime a new shell is started and one wants to use DeePMD-kit, the virtual environment should be activated by

source $tensorflow_venv/bin/activate

if one wants to skip out of the virtual environment, he/she can do

deactivate

If one has multiple python interpreters named like python3.x, it can be specified by, for example

virtualenv -p python3.7 $tensorflow_venv

If one does not need the GPU support of deepmd-kit and is concerned about package size, the CPU-only version of TensorFlow should be installed by

pip install --upgrade tensorflow-cpu

To verify the installation, run

python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

One should remember to activate the virtual environment every time he/she uses deepmd-kit.

One can also build TensorFlow Python interface from source for custom hardward optimization, such as CUDA, ROCM, or OneDNN support.

Install the DeePMD-kit’s python interface

Check the compiler version on your machine

gcc --version

The compiler gcc 4.8 or later is supported in the DeePMD-kit. Note that TensorFlow may have specific requirement of the compiler version. It is recommended to use the same compiler version as TensorFlow, which can be printed by python -c "import tensorflow;print(tensorflow.version.COMPILER_VERSION)".

Execute

cd $deepmd_source_dir
pip install .

One may set the following environment variables before executing pip:

Environment variables	Allowed value	Default value	Usage
DP_VARIANT	`cpu`, `cuda`, `rocm`	`cpu`	Build CPU variant or GPU variant with CUDA or ROCM support.
CUDA_TOOLKIT_ROOT_DIR	Path	Detected automatically	The path to the CUDA toolkit directory. CUDA 7.0 or later is supported. NVCC is required.
ROCM_ROOT	Path	Detected automatically	The path to the ROCM toolkit directory.

To test the installation, one should firstly jump out of the source directory

cd /some/other/workspace

then execute

dp -h

It will print the help information like

usage: dp [-h] {train,freeze,test} ...

DeePMD-kit: A deep learning package for many-body potential energy
representation and molecular dynamics

optional arguments:
  -h, --help           show this help message and exit

Valid subcommands:
  {train,freeze,test}
    train              train a model
    freeze             freeze the model
    test               test the model

Install horovod and mpi4py

Horovod and mpi4py is used for parallel training. For better performance on GPU, please follow tuning steps in Horovod on GPU.

# With GPU, prefer NCCL as a communicator.
HOROVOD_WITHOUT_GLOO=1 HOROVOD_WITH_TENSORFLOW=1 HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl pip install horovod mpi4py

If your work in CPU environment, please prepare runtime as below:

# By default, MPI is used as communicator.
HOROVOD_WITHOUT_GLOO=1 HOROVOD_WITH_TENSORFLOW=1 pip install horovod mpi4py

To ensure Horovod has been built with proper framework support enabled, one can invoke the horovodrun --check-build command, e.g.,

$ horovodrun --check-build

Horovod v0.22.1:

Available Frameworks:
    [X] TensorFlow
    [X] PyTorch
    [ ] MXNet

Available Controllers:
    [X] MPI
    [X] Gloo

Available Tensor Operations:
    [X] NCCL
    [ ] DDL
    [ ] CCL
    [X] MPI
    [X] Gloo

From version 2.0.1, Horovod and mpi4py with MPICH support is shipped with the installer.

If you don’t install horovod, DeePMD-kit will fall back to serial mode.

Install the C++ interface

If one does not need to use DeePMD-kit with Lammps or I-Pi, then the python interface installed in the previous section does everything and he/she can safely skip this section.

Install the Tensorflow’s C++ interface

The C++ interface of DeePMD-kit was tested with compiler gcc >= 4.8. It is noticed that the I-Pi support is only compiled with gcc >= 4.8. Note that TensorFlow may have specific requirement of the compiler version.

First the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be consistent with the python interface. You may follow the instruction or run the script $deepmd_source_dir/source/install/build_tf.py to install the corresponding C++ interface.

Install the DeePMD-kit’s C++ interface

Now go to the source code directory of DeePMD-kit and make a build place.

cd $deepmd_source_dir/source
mkdir build 
cd build

I assume you want to install DeePMD-kit into path $deepmd_root, then execute cmake

cmake -DTENSORFLOW_ROOT=$tensorflow_root -DCMAKE_INSTALL_PREFIX=$deepmd_root ..

where the variable tensorflow_root stores the location where the TensorFlow’s C++ interface is installed.

One may add the following arguments to cmake:

CMake Aurgements	Allowed value	Default value	Usage
-DTENSORFLOW_ROOT=<value>	Path	-	The Path to TensorFlow’s C++ interface.
-DCMAKE_INSTALL_PREFIX=<value>	Path	-	The Path where DeePMD-kit will be installed.
-DUSE_CUDA_TOOLKIT=<value>	`TRUE` or `FALSE`	`FALSE`	If `TRUE`, Build GPU support with CUDA toolkit.
-DCUDA_TOOLKIT_ROOT_DIR=<value>	Path	Detected automatically	The path to the CUDA toolkit directory. CUDA 7.0 or later is supported. NVCC is required.
-DUSE_ROCM_TOOLKIT=<value>	`TRUE` or `FALSE`	`FALSE`	If `TRUE`, Build GPU support with ROCM toolkit.
-DROCM_ROOT=<value>	Path	Detected automatically	The path to the ROCM toolkit directory.
-DLAMMPS_VERSION_NUMBER=<value>	Number	`20220723`	Only neccessary for LAMMPS built-in mode. The version number of LAMMPS (yyyymmdd). LAMMPS 29Oct2020 (20201029) or later is supported.
-DLAMMPS_SOURCE_ROOT=<value>	Path	-	Only neccessary for LAMMPS plugin mode. The path to the LAMMPS source code. LAMMPS 8Apr2021 or later is supported. If not assigned, the plugin mode will not be enabled.
-DUSE_TF_PYTHON_LIBS=<value>	`TRUE` or `FALSE`	`FALSE`	If `TRUE`, Build C++ interface with TensorFlow’s Python libraries(TensorFlow’s Python Interface is required). And there’s no need for building TensorFlow’s C++ interface.

If the cmake has been executed successfully, then run the following make commands to build the package:

make -j4
make install

The option -j4 means using 4 processes in parallel. You may want to use a different number according to your hardware.

If everything works fine, you will have the following executable and libraries installed in $deepmd_root/bin and $deepmd_root/lib

$ ls $deepmd_root/bin
dp_ipi      dp_ipi_low
$ ls $deepmd_root/lib
libdeepmd_cc_low.so  libdeepmd_ipi_low.so  libdeepmd_lmp_low.so  libdeepmd_low.so          libdeepmd_op_cuda.so  libdeepmd_op.so
libdeepmd_cc.so      libdeepmd_ipi.so      libdeepmd_lmp.so      libdeepmd_op_cuda_low.so  libdeepmd_op_low.so   libdeepmd.so

Install LAMMPS

There are two ways to install LAMMPS: the built-in mode and the plugin mode. The built-in mode builds LAMMPS along with the DeePMD-kit and DeePMD-kit will be loaded automatically when running LAMMPS. The plugin mode builds LAMMPS and a plugin separately, so one needs to use plugin load command to load the DeePMD-kit’s LAMMPS plugin library.

Install LAMMPS’s DeePMD-kit module (built-in mode)

Before following this section, DeePMD-kit C++ interface should have be installed.

DeePMD-kit provides a module for running MD simulation with LAMMPS. Now make the DeePMD-kit module for LAMMPS.

cd $deepmd_source_dir/source/build
make lammps

DeePMD-kit will generate a module called USER-DEEPMD in the build directory. If you need the low precision version, move env_low.sh to env.sh in the directory. Now download the LAMMPS code, and uncompress it. The LAMMPS version should be the same as what is specified as the CMAKE argument LAMMPS_VERSION_NUMBER.

cd /some/workspace
wget https://github.com/lammps/lammps/archive/stable_23Jun2022_update1.tar.gz
tar xf stable_23Jun2022_update1.tar.gz

The source code of LAMMPS is stored in directory lammps-stable_23Jun2022_update1. Now go into the LAMMPS code and copy the DeePMD-kit module like this

cd lammps-stable_23Jun2022_update1/src/
cp -r $deepmd_source_dir/source/build/USER-DEEPMD .
make yes-kspace
make yes-user-deepmd

You can enable any other package you want. Now build LAMMPS

make mpi -j4

If everything works fine, you will end up with an executable lmp_mpi.

./lmp_mpi -h

The DeePMD-kit module can be removed from LAMMPS source code by

make no-user-deepmd

Install LAMMPS (plugin mode)

Starting from 8Apr2021, LAMMPS also provides a plugin mode, allowing one to build LAMMPS and a plugin separately.

Now download the LAMMPS code (8Apr2021 or later), and uncompress it:

cd /some/workspace
wget https://github.com/lammps/lammps/archive/stable_23Jun2022_update1.tar.gz
tar xf stable_23Jun2022_update1.tar.gz

The source code of LAMMPS is stored in directory lammps-stable_23Jun2022_update1. The directory of the source code should be specified as the CMAKE argument LAMMPS_SOURCE_ROOT during installation of the DeePMD-kit C++ interface. Now go into the LAMMPS directory and create a directory called build

mkdir -p lammps-stable_23Jun2022_update1/build/
cd lammps-stable_23Jun2022_update1/build/

Now build LAMMPS. Note that PLUGIN and KSPACE package must be enabled, and BUILD_SHARED_LIBS must be set to yes. You can install any other package you want.

cmake -D PKG_PLUGIN=ON -D PKG_KSPACE=ON -D LAMMPS_INSTALL_RPATH=ON -D BUILD_SHARED_LIBS=yes -D CMAKE_INSTALL_PREFIX=${deepmd_root} -D CMAKE_INSTALL_LIBDIR=lib -D CMAKE_INSTALL_FULL_LIBDIR=${deepmd_root}/lib ../cmake
make -j4
make install

If everything works fine, you will end up with an executable ${deepmd_root}/bin/lmp.

${deepmd_root}/bin/lmp -h

Install i-PI

The i-PI works in a client-server model. The i-PI provides the server for integrating the replica positions of atoms, while the DeePMD-kit provides a client named dp_ipi that computes the interactions (including energy, force and virial). The server and client communicate via the Unix domain socket or the Internet socket. Full documentation for i-PI can be found here. The source code and a complete installation guide for i-PI can be found here. To use i-PI with already existing drivers, install and update using Pip:

pip install -U i-PI

Test with Pytest:

pip install pytest
pytest --pyargs ipi.tests

Install GROMACS with DeepMD

Before following this section, DeePMD-kit C++ interface should have be installed.

Patch source code of GROMACS

Download source code of a supported gromacs version (2020.2) from https://manual.gromacs.org/2020.2/download.html. Run the following command:

export PATH=$PATH:$deepmd_kit_root/bin
dp_gmx_patch -d $gromacs_root -v $version -p

where deepmd_kit_root is the directory where the latest version of deepmd-kit is installed, and gromacs_root refers to the source code directory of gromacs. And version represents the version of gromacs, only support 2020.2 now. If attempting to patch another version of gromacs you will still need to set version to 2020.2 as this is the only supported version, we cannot guarantee that patching other versions of gromacs will work.

Compile GROMACS with deepmd-kit

The C++ interface of deepmd-kit 2.x and tensorflow 2.x are required. And be aware that only deepmd-kit with high precision is supported now, since we cannot ensure single precision is enough for a GROMACS simulation. Here is a sample compile script:

#!/bin/bash
export CC=/usr/bin/gcc
export CXX=/usr/bin/g++
export CMAKE_PREFIX_PATH="/path/to/fftw-3.3.9" # fftw libraries
mkdir build
cd build

cmake3 .. -DCMAKE_CXX_STANDARD=14 \ # not required, but c++14 seems to be more compatible with higher version of tensorflow
          -DGMX_MPI=ON \
          -DGMX_GPU=CUDA \ # Gromacs on ROCm has not been fully developed yet
          -DCUDA_TOOLKIT_ROOT_DIR=/path/to/cuda \
          -DCMAKE_INSTALL_PREFIX=/path/to/gromacs-2020.2-deepmd
make -j
make install

Building conda packages

One may want to keep both convenience and personalization of the DeePMD-kit. To achieve this goal, one can consider building conda packages. We provide building scripts in deepmd-kit-recipes organization. These building tools are driven by conda-build and conda-smithy.

For example, if one wants to turn on MPIIO package in LAMMPS, go to lammps-feedstock repository and modify recipe/build.sh. -D PKG_MPIIO=OFF should be changed to -D PKG_MPIIO=ON. Then go to the main directory and executing

./build-locally.py

This requires that Docker has been installed. After the building, the packages will be generated in build_artifacts/linux-64 and build_artifacts/noarch, and then one can install then executing

conda create -n deepmd lammps -c file:///path/to/build_artifacts -c https://conda.deepmodeling.com -c nvidia

One may also upload packages to one’s Anaconda channel, so they can be installed on other machines:

anaconda upload /path/to/build_artifacts/linux-64/*.tar.bz2 /path/to/build_artifacts/noarch/*.tar.bz2

Data

In this section, we will introduce how to convert the DFT labeled data into the data format used by DeePMD-kit.

The DeePMD-kit organize data in systems. Each system is composed by a number of frames. One may roughly view a frame as a snap shot on an MD trajectory, but it does not necessary come from an MD simulation. A frame records the coordinates and types of atoms, cell vectors if the periodic boundary condition is assumed, energy, atomic forces and virial. It is noted that the frames in one system share the same number of atoms with the same type.

System

DeePMD-kit takes a system as data structure. A snapshot of a system is called a frame. A system may contain multiple frames with the same atom types and numbers, i.e. the same formula (like H2O). To contains data with different formula, one usually need to divide data into multiple systems, which may sometimes result in sparse-frame systems. See a new system format to further combine different systems with the same atom numbers, when training with descriptor se_atten.

A system should contain system properties, input frame properties, and labeled frame properties. The system property contains the following property:

ID	Property	Raw file	Required/Optional	Shape	Description
type	Atom type indexes	type.raw	Required	Natoms	Integers that start with 0
type_map	Atom type names	type_map.raw	Optional	Ntypes	Atom names that map to atom type, which is unnecessart to be contained in the periodic table
nopbc	Non-periodic system	nopbc	Optional	1	If True, this system is non-periodic; otherwise it’s periodic

The input frame properties contains the following property, the first axis of which is the number of frames:

ID	Property	Raw file	Unit	Required/Optional	Shape	Description
coord	Atomic coordinates	coord.raw	Å	Required	Nframes * Natoms * 3
box	Boxes	box.raw	Å	Required if periodic	Nframes * 3 * 3	in the order `XX XY XZ YX YY YZ ZX ZY ZZ`
fparam	Extra frame parameters	fparam.raw	Any	Optional	Nframes * Any
aparam	Extra atomic parameters	aparam.raw	Any	Optional	Nframes * aparam * Any

The labeled frame properties is listed as follows, all of which will be used for training if and only if the loss function contains such property:

ID	Property	Raw file	Unit	Shape	Description
energy	Frame energies	energy.raw	eV	Nframes
force	Atomic forces	force.raw	eV/Å	Nframes * Natoms * 3
virial	Frame virial	virial.raw	eV	Nframes * 9	in the order `XX XY XZ YX YY YZ ZX ZY ZZ`
atom_ener	Atomic energies	atom_ener.raw	eV	Nframes * Natoms
atom_pref	Weights of atomic forces	atom_pref.raw	1	Nframes * Natoms
dipole	Frame dipole	dipole.raw	Any	Nframes * 3
atomic_dipole	Atomic dipole	atomic_dipole.raw	Any	Nframes * Natoms * 3
polarizability	Frame polarizability	polarizability.raw	Any	Nframes * 9	in the order `XX XY XZ YX YY YZ ZX ZY ZZ`
atomic_polarizability	Atomic polarizability	atomic_polarizability.raw	Any	Nframes * Natoms * 9	in the order `XX XY XZ YX YY YZ ZX ZY ZZ`

In general, we always use the following convention of units:

Property	Unit
Time	ps
Length	Å
Energy	eV
Force	eV/Å
Virial	eV
Pressure	Bar

Formats of a system

Two binaray formats, NumPy and HDF5, are supported for training. The raw format is not directly supported, but a tool is provided to convert data from the raw format to the NumPy format.

NumPy format

In a system with the Numpy format, the system properties are stored as text files ending with .raw, such as type.raw amd type_map.raw, under the system directory. If one needs to train a non-periodic system, an empty nopbc file should be put under the system directory. Both input and labeled frame properties are saved as the NumPy binary data (NPY) files ending with .npy in each of the set.* directories. Take an example, a system may contain the following files:

type.raw
type_map.raw
nopbc
set.000/coord.npy
set.000/energy.npy
set.000/force.npy
set.001/coord.npy
set.001/energy.npy
set.001/force.npy

We assume that the atom types do not change in all frames. It is provided by type.raw, which has one line with the types of atoms written one by one. The atom types should be integers. For example the type.raw of a system that has 2 atoms with 0 and 1:

$ cat type.raw
0 1

Sometimes one needs to map the integer types to atom name. The mapping can be given by the file type_map.raw. For example

$ cat type_map.raw
O H

The type 0 is named by "O" and the type 1 is named by "H".

For training models with descriptor se_atten, a new system format is supported to put together the frame-sparse systems with the same atom number.

HDF5 format

A system with the HDF5 format has the same strucutre as the Numpy format, but in a HDF5 file, a system is organized as an HDF5 group. The file name of a Numpy file is the key in a HDF5 file, and the data is the value to the key. One need to use # in a DP path to divide the path to the HDF5 file and the HDF5 key:

/path/to/data.hdf5#H2O

Here, /path/to/data.hdf5 is the path and H2O is the key. There should be some data in the H2O group, such as H2O/type.raw and H2O/set.000/force.npy.

A HDF5 files with a large number of systems has better performance than multiple NumPy files in a large cluster.

Raw format and data conversion

A raw file is a plain text file with each information item written in one file and one frame written on one line. It’s not directly supported, but we provide a tool to convert them.

In the raw format, the property of one frame are provided per line, ending with .raw. Take an example, the default files that provide box, coordinate, force, energy and virial are box.raw, coord.raw, force.raw, energy.raw and virial.raw, respectively. Here is an example of force.raw:

$ cat force.raw
-0.724  2.039 -0.951  0.841 -0.464  0.363
 6.737  1.554 -5.587 -2.803  0.062  2.222
-1.968 -0.163  1.020 -0.225 -0.789  0.343

This force.raw contains 3 frames with each frame having the forces of 2 atoms, thus it has 3 lines and 6 columns. Each line provides all the 3 force components of 2 atoms in 1 frame. The first three numbers are the 3 force components of the first atom, while the second three numbers are the 3 force components of the second atom. Other files are organized similarly. The number of lines of all raw files should be identical.

One can use the script $deepmd_source_dir/data/raw/raw_to_set.sh to convert the prepared raw files to the NumPy format. For example, if we have a raw file that contains 6000 frames,

$ ls 
box.raw  coord.raw  energy.raw  force.raw  type.raw  virial.raw
$ $deepmd_source_dir/data/raw/raw_to_set.sh 2000
nframe is 6000
nline per set is 2000
will make 3 sets
making set 0 ...
making set 1 ...
making set 2 ...
$ ls 
box.raw  coord.raw  energy.raw  force.raw  set.000  set.001  set.002  type.raw  virial.raw

It generates three sets set.000, set.001 and set.002, with each set contains 2000 frames with the Numpy format.

Prepare data with dpdata

One can use the a convenient tool dpdata to convert data directly from the output of first principle packages to the DeePMD-kit format.

To install one can execute

pip install dpdata

An example of converting data VASP data in OUTCAR format to DeePMD-kit data can be found at

$deepmd_source_dir/examples/data_conv

Switch to that directory, then one can convert data by using the following python script

import dpdata
dsys = dpdata.LabeledSystem('OUTCAR')
dsys.to('deepmd/npy', 'deepmd_data', set_size = dsys.get_nframes())

get_nframes() method gets the number of frames in the OUTCAR, and the argument set_size enforces that the set size is equal to the number of frames in the system, viz. only one set is created in the system.

The data in DeePMD-kit format is stored in the folder deepmd_data.

A list of all supported data format and more nice features of dpdata can be found at the official website.

Model

Overall

A model has two parts, a descriptor that maps atomic configuration to a set of symmetry invariant features, and a fitting net that takes descriptor as input and predicts the atomic contribution to the target physical property. It’s defined in the model section of the input.json, for example,

    "model": {
        "type_map":	["O", "H"],
        "descriptor" :{
            "...": "..."
        },
        "fitting_net" : {
            "...": "..."
        }
    }

The two subsections, descriptor and fitting_net, define the descriptor and the fitting net, respectively.

The type_map is optional, which provides the element names (but not necessarily same with the actual name of the element) of the corresponding atom types. A model for water, as in this example, has two kinds of atoms. The atom types are internally recorded as integers, e.g., 0 for oxygen and 1 for hydrogen here. A mapping from the atom type to their names is provided by type_map.

DeePMD-kit implements the following descriptors:

se_e2_a: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes the distance between atoms as input.
se_e2_r: DeepPot-SE constructed from radial information of atomic configurations. The embedding takes the distance between atoms as input.
se_e3: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes angles between two neighboring atoms as input.
loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.
hybrid: Concate a list of descriptors to form a new descriptor.

The fitting of the following physical properties are supported

ener: Fit the energy of the system. The force (derivative with atom positions) and the virial (derivative with the box tensor) can also be trained.
dipole: The dipole moment.
polar: The polarizability.

Descriptor `"se_e2_a"`

The notation of se_e2_a is short for the Deep Potential Smooth Edition (DeepPot-SE) constructed from all information (both angular and radial) of atomic configurations. The e2 stands for the embedding with two-atoms information. This descriptor was described in detail in the DeepPot-SE paper.

In this example we will train a DeepPot-SE model for a water system. A complete training input script of this example can be find in the directory.

$deepmd_source_dir/examples/water/se_e2_a/input.json

With the training input script, data are also provided in the example directory. One may train the model with the DeePMD-kit from the directory.

The construction of the descriptor is given by section descriptor. An example of the descriptor is provided as follows

	"descriptor" :{
	    "type":		"se_e2_a",
	    "rcut_smth":	0.50,
	    "rcut":		6.00,
	    "sel":		[46, 92],
	    "neuron":		[25, 50, 100],
	    "type_one_side":	true,
	    "axis_neuron":	16,
	    "resnet_dt":	false,
	    "seed":		1
	}

The type of the descriptor is set to "se_e2_a".
rcut is the cut-off radius for neighbor searching, and the rcut_smth gives where the smoothing starts.
sel gives the maximum possible number of neighbors in the cut-off radius. It is a list, the length of which is the same as the number of atom types in the system, and sel[i] denote the maximum possible number of neighbors with type i.
The neuron specifies the size of the embedding net. From left to right the members denote the sizes of each hidden layer from input end to the output end, respectively. If the outer layer is of twice size as the inner layer, then the inner layer is copied and concatenated, then a ResNet architecture is built between them.
If the option type_one_side is set to true, then descriptor will consider the types of neighbor atoms. Otherwise, both the types of centric and neighbor atoms are considered.
The axis_neuron specifies the size of submatrix of the embedding matrix, the axis matrix as explained in the DeepPot-SE paper
If the option resnet_dt is set to true, then a timestep is used in the ResNet.
seed gives the random seed that is used to generate random numbers when initializing the model parameters.

Descriptor `"se_e2_r"`

The notation of se_e2_r is short for the Deep Potential Smooth Edition (DeepPot-SE) constructed from the radial information of atomic configurations. The e2 stands for the embedding with two-atom information.

A complete training input script of this example can be found in the directory

$deepmd_source_dir/examples/water/se_e2_r/input.json

The training input script is very similar to that of se_e2_a. The only difference lies in the descriptor section

	"descriptor": {
	    "type":		"se_e2_r",
	    "sel":		[46, 92],
	    "rcut_smth":	0.50,
	    "rcut":		6.00,
	    "neuron":		[5, 10, 20],
	    "resnet_dt":	false,
	    "seed":		1,
	    "_comment": " that's all"
	},

The type of the descriptor is set by the key type.

Descriptor `"se_e3"`

The notation of se_e3 is short for the Deep Potential Smooth Edition (DeepPot-SE) constructed from all information (both angular and radial) of atomic configurations. The embedding takes angles between two neighboring atoms as input (denoted by e3).

A complete training input script of this example can be found in the directory

$deepmd_source_dir/examples/water/se_e3/input.json

The training input script is very similar to that of se_e2_a. The only difference lies in the descriptor <model/descriptor> section

	"descriptor": {
	    "type":		"se_e3",
	    "sel":		[40, 80],
	    "rcut_smth":	0.50,
	    "rcut":		6.00,
	    "neuron":		[2, 4, 8],
	    "resnet_dt":	false,
	    "seed":		1,
	    "_comment":		" that's all"
	},

The type of the descriptor is set by the key type.

Descriptor `"se_atten"`

DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation

ALT

Here we propose DPA-1, a Deep Potential model with a novel attention mechanism, which is highly effective for representing the conformation and chemical spaces of atomic systems and learning the PES.

See this paper for more information. DPA-1 is implemented as a new descriptor "se_atten" for model training, which can be used after simply editing the input.json.

Installation

Follow the standard installation of python interface in DeePMD-kit. After that, you can smoothly use the DPA-1 model with following instructions.

Introduction to new features of DPA-1

Next we will list the detail settings in input.json and the data format, especially for large systems with dozens of elements. An example of DPA-1 input can be found in here.

Descriptor `"se_atten"`

The notation of se_atten is short for the smooth edition of Deep Potential with an attention mechanism. This descriptor was described in detail in the DPA-1 paper and the images above.

In this example we will train a DPA-1 model for a water system. A complete training input script of this example can be find in the directory:

$deepmd_source_dir/examples/water/se_atten/input.json

With the training input script, data are also provided in the example directory. One may train the model with the DeePMD-kit from the directory.

An example of the DPA-1 descriptor is provided as follows

	"descriptor" :{
          "type":		"se_atten",
          "rcut_smth":	0.50,
          "rcut":		6.00,
          "sel":		120,
          "neuron":		[25, 50, 100],
          "axis_neuron":	16,
          "resnet_dt":	false,
          "attn":	128,
          "attn_layer":	2,
          "attn_mask":	false,
          "attn_dotr":	true,
          "seed":	1
	}

The type of the descriptor is set to "se_atten", which will use DPA-1 structures.
rcut is the cut-off radius for neighbor searching, and the rcut_smth gives where the smoothing starts.
sel gives the maximum possible number of neighbors in the cut-off radius. It is an int. Note that this number highly effects the efficiency of training, which we usually use less than 200. (We use 120 for training 56 elements in OC2M dataset)
The neuron specifies the size of the embedding net. From left to right the members denote the sizes of each hidden layer from input end to the output end, respectively. If the outer layer is of twice size as the inner layer, then the inner layer is copied and concatenated, then a ResNet architecture is built between them.
The axis_neuron specifies the size of submatrix of the embedding matrix, the axis matrix as explained in the DeepPot-SE paper
If the option resnet_dt is set to true, then a timestep is used in the ResNet.
seed gives the random seed that is used to generate random numbers when initializing the model parameters.
attn sets the length of hidden vector during scale-dot attention computation.
attn_layer sets the number of layers in attention mechanism.
attn_mask determines whether to mask the diagonal in the attention weights and False is recommended.
attn_dotr determines whether to dot the relative coordinates on the attention weights as a gated scheme, True is recommended.

Fitting `"ener"`

DPA-1 only support "ener" fitting type, and you can refer here for detail information.

Type embedding

DPA-1 only support models with type embeddings on. And the default setting is as follows:

"type_embedding":{
            "neuron":           [8],
            "resnet_dt":        false,
            "seed":             1
        }

You can add these settings in input.json if you want to change the default ones, see here for detail information.

Type map

For training a large systems, especially those with dozens of elements, the type determines the element index of training data:

"type_map": [
   "Mg",
   "Al",
   "Cu"
  ]

which should include all the elements in the dataset you want to train on.

Data format

DPA-1 supports the standard data format, which is detailed in data-conv.md and system.md. Note that in this format, only those frames with the same fingerprint(i.e. the number of atoms of different element) can be put together as a unit system. This may lead to sparse frame number in those rare systems.

An ideal way is to put systems with same total number of atoms together, which is the way we trained DPA-1 on OC2M. This system format, which is called mixed_type, is proper to put frame-sparse systems together and is slightly different from the standard one. Take an example, a mixed_type may contain the following files:

type.raw
type_map.raw
set.000/box.npy
set.000/coord.npy
set.000/energy.npy
set.000/force.npy
set.000/real_atom_types.npy

This system contains Nframes frames with the same atom number Natoms, the total number of element types contained in all frames is Ntypes. Note that we put all the frames in one set set.000. Most files are the same as those in standard format, here we only list the distinct ones:

ID	Property	File	Required/Optional	Shape	Description
/	Atom type indexes (place holder)	type.raw	Required	Natoms	All zeros to fake the type input
type_map	Atom type names	type_map.raw	Required	Ntypes	Atom names that map to atom type contained in all the frames, which is unnecessart to be contained in the periodic table
type	Atom type indexes of each frame	real_atom_types.npy	Required	Nframes * Natoms	Integers that describe atom types in each frame, corresponding to indexes in type_map

With these edited files, one can put together frames with the same Natoms, instead of the same formula (like H2O). Note that this mixed_type format only supports se_atten descriptor.

The API to generate or transfer to mixed_type format will be uploaded on dpdata soon for a more convenient experience.

Training example

Here we upload the AlMgCu example showed in the paper, you can download here: Baidu disk; Google disk.

Descriptor `"hybrid"`

This descriptor hybridize multiple descriptors to form a new descriptor. For example we have a list of descriptor denoted by $\mathcal D_1$, $\mathcal D_2$, …, $\mathcal D_N$, the hybrid descriptor this the concatenation of the list, i.e. $\mathcal D = (\mathcal D_1, \mathcal D_2, \cdots, \mathcal D_N)$.

To use the descriptor in DeePMD-kit, one firstly set the type to hybrid, then provide the definitions of the descriptors by the items in the list,

        "descriptor" :{
            "type": "hybrid",
            "list" : [
                {
		    "type" : "se_e2_a",
		    ...		    
                },
                {
		    "type" : "se_e2_r",
		    ...
                }
            ]
        },

A complete training input script of this example can be found in the directory

$deepmd_source_dir/examples/water/hybrid/input.json

Determine `sel`

All descriptors require to set sel, which means the expected maximum number of type-i neighbors of an atom. DeePMD-kit will allocate memory according to sel.

sel should not be too large or too small. If sel is too large, the computing will become much slower and cost more memory. If sel is not enough, the energy will be not conserved, making the accuracy of the model worse.

To determine a proper sel, one can calculate the neighbor stat of the training data before training:

dp neighbor-stat -s data -r 6.0 -t O H

where data is the directory of data, 6.0 is the cutoff radius, and O and H is the type map. The program will give the max_nbor_size. For example, max_nbor_size of the water example is [38, 72], meaning an atom may have 38 O neighbors and 72 H neighbors in the training data.

The sel should be set to a higher value than that of the training data, considering there may be some extreme geometries during MD simulations. As a result, we set to [46, 92] in the water example.

Fit energy

In this section, we will take $deepmd_source_dir/examples/water/se_e2_a/input.json as an example of the input file.

The fitting network

The construction of the fitting net is give by section fitting_net

	"fitting_net" : {
	    "neuron":		[240, 240, 240],
	    "resnet_dt":	true,
	    "seed":		1
	},

neuron specifies the size of the fitting net. If two neighboring layers are of the same size, then a ResNet architecture is built between them.
If the option resnet_dt is set to true, then a timestep is used in the ResNet.
seed gives the random seed that is used to generate random numbers when initializing the model parameters.

Loss

The loss function $L$ for training energy is given by

\[L = p_e L_e + p_f L_f + p_v L_v\]

where $L_e$, $L_f$, and $L_v$ denote the loss in energy, force and virial, respectively. $p_e$, $p_f$, and $p_v$ give the prefactors of the energy, force and virial losses. The prefectors may not be a constant, rather it changes linearly with the learning rate. Taking the force prefactor for example, at training step $t$, it is given by

\[p_f(t) = p_f^0 \frac{ \alpha(t) }{ \alpha(0) } + p_f^\infty ( 1 - \frac{ \alpha(t) }{ \alpha(0) })\]

where $\alpha(t)$ denotes the learning rate at step $t$. $p_f^0$ and $p_f^\infty$ specifies the $p_f$ at the start of the training and at the limit of $t \to \infty$ (set by start_pref_f and limit_pref_f, respectively), i.e.

pref_f(t) = start_pref_f * ( lr(t) / start_lr ) + limit_pref_f * ( 1 - lr(t) / start_lr )

The loss section in the input.json is

    "loss" : {
	"start_pref_e":	0.02,
	"limit_pref_e":	1,
	"start_pref_f":	1000,
	"limit_pref_f":	1,
	"start_pref_v":	0,
	"limit_pref_v":	0
    }

The options start_pref_e, limit_pref_e, start_pref_f, limit_pref_f, start_pref_v and limit_pref_v determine the start and limit prefactors of energy, force and virial, respectively.

If one does not want to train with virial, then he/she may set the virial prefactors start_pref_v and limit_pref_v to 0.

Fit `tensor` like `Dipole` and `Polarizability`

Unlike energy, which is a scalar, one may want to fit some high dimensional physical quantity, like dipole (vector) and polarizability (matrix, shorted as polar). Deep Potential has provided different APIs to do this. In this example, we will show you how to train a model to fit them for a water system. A complete training input script of the examples can be found in

$deepmd_source_dir/examples/water_tensor/dipole/dipole_input.json
$deepmd_source_dir/examples/water_tensor/polar/polar_input.json

The training and validation data are also provided our examples. But note that the data provided along with the examples are of limited amount, and should not be used to train a production model.

Similar to the input.json used in ener mode, training json is also divided into model, learning_rate, loss and training. Most keywords remains the same as ener mode, and their meaning can be found here. To fit a tensor, one need to modify model/fitting_net and loss.

The fitting Network

The fitting_net section tells DP which fitting net to use.

The json of dipole type should be provided like

	"fitting_net" : {
		"type": "dipole",
		"sel_type": [0],
		"neuron": [100,100,100],
		"resnet_dt": true,
		"seed": 1,
	},

The json of polar type should be provided like

	"fitting_net" : {
	   	"type": "polar",
		"sel_type": [0],
		"neuron": [100,100,100],
		"resnet_dt": true,
		"seed": 1,
	},

type specifies which type of fitting net should be used. It should be either dipole or polar. Note that global_polar mode in version 1.x is already deprecated and is merged into polar. To specify whether a system is global or atomic, please see here.
sel_type is a list specifying which type of atoms have the quantity you want to fit. For example, in water system, sel_type is [0] since 0 represents for atom O. If left unset, all type of atoms will be fitted.
The rest args has the same meaning as they do in ener mode.

Loss

DP supports a combinational training of global system (only a global tensor label, i.e. dipole or polar, is provided in a frame) and atomic system (labels for each atom included in sel_type are provided). In a global system, each frame has just one tensor label. For example, when fitting polar, each frame will just provide a 1 x 9 vector which gives the elements of the polarizability tensor of that frame in order XX, XY, XZ, YX, YY, YZ, XZ, ZY, ZZ. By contrast, in a atomic system, each atom in sel_type has a tensor label. For example, when fitting dipole, each frame will provide a #sel_atom x 3 matrix, where #sel_atom is the number of atoms whose type are in sel_type.

The loss section tells DP the weight of this two kind of loss, i.e.

loss = pref * global_loss + pref_atomic * atomic_loss

The loss section should be provided like

	"loss" : {
		"type":		"tensor",
		"pref":		1.0,
		"pref_atomic":	1.0
	},

type should be written as tensor as a distinction from ener mode.
pref and pref_atomic respectively specify the weight of global loss and atomic loss. It can not be left unset. If set to 0, system with corresponding label will NOT be included in the training process.

Training Data Preparation

In tensor mode, the identification of label’s type (global or atomic) is derived from the file name. The global label should be named as dipole.npy/raw or polarizability.npy/raw, while the atomic label should be named as atomic_dipole.npy/raw or atomic_polarizability.npy/raw. If wrongly named, DP will report an error

ValueError: cannot reshape array of size xxx into shape (xx,xx). This error may occur when your label mismatch it's name, i.e. you might store global tensor in `atomic_tensor.npy` or atomic tensor in `tensor.npy`.

In this case, please check the file name of label.

Train the Model

The training command is the same as ener mode, i.e.

dp train input.json

The detailed loss can be found in lcurve.out:

#  step    rmse_val   rmse_trn  rmse_lc_val rmse_lc_trn rmse_gl_val rmse_gl_trn  lr
   8.34e+00   8.26e+00   8.34e+00   8.26e+00    0.00e+00    0.00e+00   1.0e-02
   3.51e-02   8.55e-02   0.00e+00   8.55e-02    4.38e-03    0.00e+00   5.0e-03
   4.77e-02   5.61e-02   0.00e+00   5.61e-02    5.96e-03    0.00e+00   2.5e-03
   5.68e-02   1.47e-02   0.00e+00   0.00e+00    7.10e-03    1.84e-03   1.3e-03
   3.73e-02   3.48e-02   1.99e-02   0.00e+00    2.18e-03    4.35e-03   6.3e-04
   2.77e-02   5.82e-02   1.08e-02   5.82e-02    2.11e-03    0.00e+00   3.2e-04
   2.81e-02   5.43e-02   2.01e-02   0.00e+00    1.01e-03    6.79e-03   1.6e-04
   2.97e-02   3.28e-02   2.03e-02   0.00e+00    1.17e-03    4.10e-03   7.9e-05
   2.25e-02   6.19e-02   9.05e-03   0.00e+00    1.68e-03    7.74e-03   4.0e-05
   3.18e-02   5.54e-02   9.93e-03   5.54e-02    2.74e-03    0.00e+00   2.0e-05
   2.63e-02   5.02e-02   1.02e-02   5.02e-02    2.01e-03    0.00e+00   1.0e-05
   3.27e-02   5.89e-02   2.13e-02   5.89e-02    1.43e-03    0.00e+00   5.0e-06
   2.85e-02   2.42e-02   2.85e-02   0.00e+00    0.00e+00    3.02e-03   2.5e-06
   3.47e-02   5.71e-02   1.07e-02   5.71e-02    3.00e-03    0.00e+00   1.3e-06
   3.13e-02   5.76e-02   3.13e-02   5.76e-02    0.00e+00    0.00e+00   6.3e-07
   3.34e-02   1.11e-02   2.09e-02   0.00e+00    1.57e-03    1.39e-03   3.2e-07
   3.11e-02   5.64e-02   3.11e-02   5.64e-02    0.00e+00    0.00e+00   1.6e-07
   2.97e-02   5.05e-02   2.97e-02   5.05e-02    0.00e+00    0.00e+00   7.9e-08
   2.64e-02   7.70e-02   1.09e-02   0.00e+00    1.94e-03    9.62e-03   4.0e-08
   3.28e-02   2.56e-02   3.28e-02   0.00e+00    0.00e+00    3.20e-03   2.0e-08
   2.59e-02   5.71e-02   1.03e-02   5.71e-02    1.94e-03    0.00e+00   1.0e-08

One may notice that in each step, some of local loss and global loss will be 0.0. This is because our training data and validation data consist of global system and atomic system, i.e.

	--training_data
		>atomic_system
		>global_system
	--validation_data
		>atomic_system
		>global_system

During training, at each step when the lcurve.out is printed, the system used for evaluating the training (validation) error may be either with only global or only atomic labels, thus the corresponding atomic or global errors are missing and are printed as zeros.

Type embedding approach

We generate specific type embedding vector for each atom type, so that we can share one descriptor embedding net and one fitting net in total, which decline training complexity largely.

The training input script is similar to that of se_e2_a, but different by adding the type_embedding section.

Type embedding net

The model defines how the model is constructed, adding a section of type embedding net:

    "model": {
	"type_map":	["O", "H"],
	"type_embedding":{
			...
	},	    
	"descriptor" :{
            ...
	},
	"fitting_net" : {
            ...
	}
    }

Model will automatically apply type embedding approach and generate type embedding vectors. If type embedding vector is detected, descriptor and fitting net would take it as a part of input.

The construction of type embedding net is given by type_embedding. An example of type_embedding is provided as follows

	"type_embedding":{
	    "neuron":		[2, 4, 8],
	    "resnet_dt":	false,
	    "seed":		1
	}

The neuron specifies the size of the type embedding net. From left to right the members denote the sizes of each hidden layer from input end to the output end, respectively. It takes one-hot vector as input and output dimension equals to the last dimension of the neuron list. If the outer layer is of twice size as the inner layer, then the inner layer is copied and concatenated, then a ResNet architecture is built between them.
If the option resnet_dt is set to true, then a timestep is used in the ResNet.
seed gives the random seed that is used to generate random numbers when initializing the model parameters.

A complete training input script of this example can be find in the directory.

$deepmd_source_dir/examples/water/se_e2_a_tebd/input.json

See here for further explanation of type embedding.

Note

You can’t apply compression method while using atom type embedding.

Deep potential long-range (DPLR)

Notice: The interfaces of DPLR are not stable and subject to change

The method of DPLR is described in this paper. One is recommended to read the paper before using the DPLR.

In the following, we take the DPLR model for example to introduce the training and LAMMPS simulation with the DPLR model. The DPLR model is training in two steps.

Train a deep Wannier model for Wannier centroids

We use the deep Wannier model (DW) to represent the relative position of the Wannier centroid (WC) with the atom to which it is associated. One may consult the introduction of the dipole model for a detailed introduction. An example input wc.json and a small dataset data for tutorial purposes can be found in

$deepmd_source_dir/examples/water/dplr/train/

It is noted that the tutorial dataset is not enough for training a productive model. Two settings make the training input script different from an energy training input:

	"fitting_net": {
	    "type":		"dipole",
	    "dipole_type":	[0],
	    "neuron":		[128, 128, 128],
	    "seed":		1
	},

The type of fitting is set to dipole. The dipole is associate to type 0 atoms (oxygens), by the setting "dipole_type": [0]. What we trained is the displacement of the WC from the corresponding oxygen atom. It shares the same training input as atomic dipole because both are 3-dimensional vectors defined on atoms. The loss section is provided as follows

    "loss": {
	"type":		"tensor",
	"pref":		0.0,
	"pref_atomic":	1.0
    },

so that the atomic dipole is trained as labels. Note that the numpy compressed file atomic_dipole.npy should be provided in each dataset.

The training and freezing can be started from the example directory by

dp train dw.json && dp freeze -o dw.pb

Train the DPLR model

The training of the DPLR model is very similar to the standard short-range DP models. An example input script can be found in the example directory. The following section is introduced to compute the long-range energy contribution of the DPLR model, and modify the short-range DP model by this part.

        "modifier": {
            "type":             "dipole_charge",
            "model_name":       "dw.pb",
            "model_charge_map": [-8],
            "sys_charge_map":   [6, 1],
            "ewald_h":          1.00,
            "ewald_beta":       0.40
        },

The model_name specifies which DW model is used to predict the position of WCs. model_charge_map gives the amount of charge assigned to WCs. sys_charge_map provides the nuclear charge of oxygen (type 0) and hydrogen (type 1) atoms. ewald_beta (unit $\text{Å}^{-1}$) gives the spread parameter controls the spread of Gaussian charges, and ewald_h (unit Å) assigns the grid size of Fourier transform. The DPLR model can be trained and frozen by (from the example directory)

dp train ener.json && dp freeze -o ener.pb

Molecular dynamics simulation with DPLR

In MD simulations, the long-range part of the DPLR is calculated by the LAMMPS kspace support. Then the long-range interaction is back-propagated to atoms by DeePMD-kit. This setup is commonly used in classical molecular dynamics simulations as the “virtual site”. Unfortunately, LAMMPS does not natively support virtual sites, so we have to hack the LAMMPS code, which makes the input configuration and script a little wired.

An example of input configuration file and script can be found in

$deepmd_source_dir/examples/water/dplr/lmp/

We use atom_style full for DPLR simulations. the coordinates of the WCs are explicitly written to the configuration file. Moreover, a virtual bond is established between the oxygens and the WCs to indicate they are associated together. The configuration file containing 128 H2O molecules is thus written as

512 atoms
3 atom types
128 bonds
1 bond types

0 16.421037674 xlo xhi
0 16.421037674 ylo yhi
0 16.421037674 zlo zhi
0 0 0 xy xz yz

Masses

1 16
2 2 
3 16

Atoms

       1        1 1  6 8.4960699081e+00 7.5073699951e+00 9.6371297836e+00
       2        2 1  6 4.0597701073e+00 6.8156299591e+00 1.2051420212e+01
...

     385        1 3 -8 8.4960699081e+00 7.5073699951e+00 9.6371297836e+00
     386        2 3 -8 4.0597701073e+00 6.8156299591e+00 1.2051420212e+01
...

Bonds

1 1 1 385
2 1 2 386
...

The oxygens and hydrogens are assigned with atom types 1 and 2 (corresponding to training atom types 0 and 1), respectively. The WCs are assigned with atom type 3. We want to simulate heavy water so the mass of hydrogens is set to 2.

An example input script is provided in

$deepmd_source_dir/examples/water/dplr/lmp/in.lammps

Here are some explanations

# groups of real and virtual atoms
group           real_atom type 1 2
group           virtual_atom type 3

# bond between real and its corresponding virtual site should be given
# to setup a map between real and virtual atoms. However, no real
# bonded interaction is applied, thus bond_sytle "zero" is used.
pair_style      deepmd ener.pb
pair_coeff      * *
bond_style      zero
bond_coeff      *
special_bonds   lj/coul 1 1 1 angle no

Type 1 and 2 (O and H) are real_atoms, while type 3 (WCs) are virtual_atoms. The model file ener.pb stores both the DW and DPLR models, so the position of WCs and the energy can be inferred from it. A virtual bond type is specified by bond_style zero. The special_bonds command switches off the exclusion of intramolecular interactions.

# kspace_style "pppm/dplr" should be used. in addition the
# gewald(1/distance) should be set the same as that used in
# training. Currently only ik differentiation is supported.
kspace_style	pppm/dplr 1e-5
kspace_modify	gewald ${BETA} diff ik mesh ${KMESH} ${KMESH} ${KMESH}

The long-range part is calculated by the kspace support of LAMMPS. The kspace_style pppm/dplr is required. The spread parameter set by variable BETA should be set the same as that used in training. The KMESH should be set dense enough so the long-range calculation is converged.

# "fix dplr" set the position of the virtual atom, and spread the
# electrostatic interaction asserting on the virtual atom to the real
# atoms. "type_associate" associates the real atom type its
# corresponding virtual atom type. "bond_type" gives the type of the
# bond between the real and virtual atoms.
fix		0 all dplr model ener.pb type_associate 1 3 bond_type 1
fix_modify	0 virial yes

The fix command dplr calculates the position of WCs by the DW model and back-propagates the long-range interaction on virtual atoms to real toms.

# compute the temperature of real atoms, excluding virtual atom contribution
compute		real_temp real_atom temp
compute		real_press all pressure real_temp
fix		1 real_atom nvt temp ${TEMP} ${TEMP} ${TAU_T}
fix_modify	1 temp real_temp

The temperature of the system should be computed from the real atoms. The kinetic contribution in the pressure tensor is also computed from the real atoms. The thermostat is applied to only real atoms. The computed temperature and pressure of real atoms can be accessed by, e.g.

fix             thermo_print all print ${THERMO_FREQ} "$(step) $(pe) $(ke) $(etotal) $(enthalpy) $(c_real_temp) $(c_real_press) $(vol) $(c_real_press[1]) $(c_real_press[2]) $(c_real_press[3])" append thermo.out screen no title "# step pe ke etotal enthalpy temp press vol pxx pyy pzz"

The LAMMPS simulation can be started from the example directory by

lmp -i in.lammps

If LAMMPS complains that no model file ener.pb exists, it can be copied from the training example directory.

The MD simulation lasts for only 20 steps. If one runs a longer simulation, it will blow up, because the model is trained with a very limited dataset for a very short training steps, thus is of poor quality.

Another restriction should be noted is that the energies printed at the zero step is not correct. This is because at the zero step the position of the WC has not been updated with the DW model. The energies printed in later steps are correct.

Deep Potential - Range Correction (DPRc)

Deep Potential - Range Correction (DPRc) is designed to combine with QM/MM method, and corrects energies from a low-level QM/MM method to a high-level QM/MM method:

\[ E=E_\text{QM}(\mathbf R; \mathbf P) + E_\text{QM/MM}(\mathbf R; \mathbf P) + E_\text{MM}(\mathbf R) + E_\text{DPRc}(\mathbf R) \]

See the JCTC paper for details.

Training data

Instead the normal ab initio data, one needs to provide the correction from a low-level QM/MM method to a high-level QM/MM method:

\[ E = E_\text{high-level QM/MM} - E_\text{low-level QM/MM} \]

Two levels of data use the same MM method, so $E_\text{MM}$ is eliminated.

Training the DPRc model

In a DPRc model, QM atoms and MM atoms have different atom types. Assuming we have 4 QM atom types (C, H, O, P) and 2 MM atom types (HW, OW):

"type_map": ["C", "H", "HW", "O", "OW", "P"]

As described in the paper, the DPRc model only corrects $E_\text{QM}$ and $E_\text{QM/MM}$ within the cutoff, so we use a hybrid descriptor to describe them separatedly:

"descriptor" :{
    "type":             "hybrid",
    "list" : [
        {
            "type":     "se_e2_a",
            "sel":              [6, 11, 0, 6, 0, 1],
            "rcut_smth":        1.00,
            "rcut":             9.00,
            "neuron":           [12, 25, 50],
            "exclude_types":    [[2, 2], [2, 4], [4, 4], [0, 2], [0, 4], [1, 2], [1, 4], [3, 2], [3, 4], [5, 2], [5, 4]],
            "axis_neuron":      12,
            "set_davg_zero":    true,
            "_comment": " QM/QM interaction"
        },
        {
            "type":     "se_e2_a",
            "sel":              [6, 11, 100, 6, 50, 1],
            "rcut_smth":        0.50,
            "rcut":             6.00,
            "neuron":           [12, 25, 50],
            "exclude_types":    [[0, 0], [0, 1], [0, 3], [0, 5], [1, 1], [1, 3], [1, 5], [3, 3], [3, 5], [5, 5], [2, 2], [2, 4], [4, 4]],
            "axis_neuron":      12,
            "set_davg_zero":    true,
            "_comment": " QM/MM interaction"
        }
    ]
}

exclude_types can be generated by the following Python script:

from itertools import combinations_with_replacement, product
qm = (0, 1, 3, 5)
mm = (2, 4)
print("QM/QM:", list(map(list, list(combinations_with_replacement(mm, 2)) + list(product(qm, mm)))))
print("QM/MM:", list(map(list, list(combinations_with_replacement(qm, 2)) + list(combinations_with_replacement(mm, 2)))))

Also, DPRc assumes MM atom energies (atom_ener) are zero:

"fitting_net": {
   "neuron": [240, 240, 240],
   "resnet_dt": true,
   "atom_ener": [null, null, 0.0, null, 0.0, null]
}

Note that atom_ener only works when descriptor/set_davg_zero is true.

Run MD simulations

The DPRc model has the best practices with the AMBER QM/MM module. An example is given by GitLab RutgersLBSR/AmberDPRc. In theory, DPRc is able to be used with any QM/MM package, as long as the DeePMD-kit package accepts QM atoms and MM atoms within the cutoff range and returns energies and forces.

Training

Train a model

Several examples of training can be found at the examples directory:

$ cd $deepmd_source_dir/examples/water/se_e2_a/

After switching to that directory, the training can be invoked by

$ dp train input.json

where input.json is the name of the input script.

By default, the verbosity level of the DeePMD-kit is INFO, one may see a lot of important information on the code and environment showing on the screen. Among them two pieces of information regarding data systems worth special notice.

DEEPMD INFO    ---Summary of DataSystem: training     -----------------------------------------------
DEEPMD INFO    found 3 system(s):
DEEPMD INFO                                        system  natoms  bch_sz   n_bch   prob  pbc
DEEPMD INFO                         ../data_water/data_0/     192       1      80  0.250    T
DEEPMD INFO                         ../data_water/data_1/     192       1     160  0.500    T
DEEPMD INFO                         ../data_water/data_2/     192       1      80  0.250    T
DEEPMD INFO    --------------------------------------------------------------------------------------
DEEPMD INFO    ---Summary of DataSystem: validation   -----------------------------------------------
DEEPMD INFO    found 1 system(s):
DEEPMD INFO                                        system  natoms  bch_sz   n_bch   prob  pbc
DEEPMD INFO                          ../data_water/data_3     192       1      80  1.000    T
DEEPMD INFO    --------------------------------------------------------------------------------------

The DeePMD-kit prints detailed information on the training and validation data sets. The data sets are defined by training_data and validation_data defined in the training section of the input script. The training data set is composed by three data systems, while the validation data set is composed by one data system. The number of atoms, batch size, number of batches in the system and the probability of using the system are all shown on the screen. The last column presents if the periodic boundary condition is assumed for the system.

During the training, the error of the model is tested every disp_freq training steps with the batch used to train the model and with numb_btch batches from the validating data. The training error and validation error are printed correspondingly in the file disp_file (default is lcurve.out). The batch size can be set in the input script by the key batch_size in the corresponding sections for training and validation data set. An example of the output

#  step      rmse_val    rmse_trn    rmse_e_val  rmse_e_trn    rmse_f_val  rmse_f_trn         lr
    3.33e+01    3.41e+01      1.03e+01    1.03e+01      8.39e-01    8.72e-01    1.0e-03
    2.57e+01    2.56e+01      1.87e+00    1.88e+00      8.03e-01    8.02e-01    1.0e-03
    2.45e+01    2.56e+01      2.26e-01    2.21e-01      7.73e-01    8.10e-01    1.0e-03
    1.62e+01    1.66e+01      5.01e-02    4.46e-02      5.11e-01    5.26e-01    1.0e-03
    1.36e+01    1.32e+01      1.07e-02    2.07e-03      4.29e-01    4.19e-01    1.0e-03
    1.07e+01    1.05e+01      2.45e-03    4.11e-03      3.38e-01    3.31e-01    1.0e-03

The file contains 8 columns, form left to right, are the training step, the validation loss, training loss, root mean square (RMS) validation error of energy, RMS training error of energy, RMS validation error of force, RMS training error of force and the learning rate. The RMS error (RMSE) of the energy is normalized by number of atoms in the system. One can visualize this file by a simple Python script:

import numpy as np
import matplotlib.pyplot as plt

data = np.genfromtxt("lcurve.out", names=True)
for name in data.dtype.names[1:-1]:
    plt.plot(data['step'], data[name], label=name)
plt.legend()
plt.xlabel('Step')
plt.ylabel('Loss')
plt.xscale('symlog')
plt.yscale('log')
plt.grid()
plt.show()

Checkpoints will be written to files with prefix save_ckpt every save_freq training steps.

Warning

It is warned that the example water data (in folder examples/water/data) is of very limited amount, is provided only for testing purpose, and should not be used to train a production model.

Advanced options

In this section, we will take $deepmd_source_dir/examples/water/se_e2_a/input.json as an example of the input file.

Learning rate

The learning_rate section in input.json is given as follows

    "learning_rate" :{
	"type":		"exp",
	"start_lr":	0.001,
	"stop_lr":	3.51e-8,
	"decay_steps":	5000,
	"_comment":	"that's all"
    }

start_lr gives the learning rate at the beginning of the training.
stop_lr gives the learning rate at the end of the training. It should be small enough to ensure that the network parameters satisfactorily converge.
During the training, the learning rate decays exponentially from start_lr to stop_lr following the formula:

\[ \alpha(t) = \alpha_0 \lambda ^ { t / \tau } \]

where $t$ is the training step, $\alpha$ is the learning rate, $\alpha_0$ is the starting learning rate (set by start_lr), $\lambda$ is the decay rate, and $\tau$ is the decay steps, i.e.

```
lr(t) = start_lr * decay_rate ^ ( t / decay_steps )
```

Training parameters

Other training parameters are given in the training section.

    "training": {
 	"training_data": {
	    "systems":		["../data_water/data_0/", "../data_water/data_1/", "../data_water/data_2/"],
	    "batch_size":	"auto"
	},
	"validation_data":{
	    "systems":		["../data_water/data_3"],
	    "batch_size":	1,
	    "numb_btch":	3
	},
	"mixed_precision": {
	    "output_prec":      "float32",
	    "compute_prec":     "float16"
	},

	"numb_steps":	1000000,
	"seed":		1,
	"disp_file":	"lcurve.out",
	"disp_freq":	100,
	"save_freq":	1000
    }

The sections training_data and validation_data give the training dataset and validation dataset, respectively. Taking the training dataset for example, the keys are explained below:

systems provide paths of the training data systems. DeePMD-kit allows you to provide multiple systems with different numbers of atoms. This key can be a list or a str.
- list: systems gives the training data systems.
- str: systems should be a valid path. DeePMD-kit will recursively search all data systems in this path.
At each training step, DeePMD-kit randomly pick batch_size frame(s) from one of the systems. The probability of using a system is by default in proportion to the number of batches in the system. More optional are available for automatically determining the probability of using systems. One can set the key auto_prob to
- "prob_uniform" all systems are used with the same probability.
- "prob_sys_size" the probability of using a system is in proportional to its size (number of frames).
- "prob_sys_size; sidx_0:eidx_0:w_0; sidx_1:eidx_1:w_1;..." the list of systems are divided into blocks. The block i has systems ranging from sidx_i to eidx_i. The probability of using a system from block i is in proportional to w_i. Within one block, the probability of using a system is in proportional to its size.
An example of using "auto_prob" is given as below. The probability of using systems[2] is 0.4, and the sum of the probabilities of using systems[0] and systems[1] is 0.6. If the number of frames in systems[1] is twice as system[0], then the probability of using system[1] is 0.4 and that of system[0] is 0.2.

 	"training_data": {
	    "systems":		["../data_water/data_0/", "../data_water/data_1/", "../data_water/data_2/"],
	    "auto_prob":	"prob_sys_size; 0:2:0.6; 2:3:0.4",
	    "batch_size":	"auto"
	}

The probability of using systems can also be specified explicitly with key sys_probs that is a list having the length of the number of systems. For example

 	"training_data": {
	    "systems":		["../data_water/data_0/", "../data_water/data_1/", "../data_water/data_2/"],
	    "sys_probs":	[0.5, 0.3, 0.2],
	    "batch_size":	"auto:32"
	}

The key batch_size specifies the number of frames used to train or validate the model in a training step. It can be set to
- list: the length of which is the same as the systems. The batch size of each system is given by the elements of the list.
- int: all systems use the same batch size.
- "auto": the same as "auto:32", see "auto:N"
- "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.
The key numb_batch in validate_data gives the number of batches of model validation. Note that the batches may not be from the same system

The section mixed_precision specifies the mixed precision settings, which will enable the mixed precision training workflow for deepmd-kit. The keys are explained below:

output_prec precision used in the output tensors, only float32 is supported currently.
compute_prec precision used in the computing tensors, only float16 is supported currently. Note there are severial limitations about the mixed precision training:
Only se_e2_a type descriptor is supported by the mixed precision training workflow.
The precision of embedding net and fitting net are forced to be set to float32.

Other keys in the training section are explained below:

numb_steps The number of training steps.
seed The random seed for getting frames from the training data set.
disp_file The file for printing learning curve.
disp_freq The frequency of printing learning curve. Set in the unit of training steps
save_freq The frequency of saving check point.

Options and environment variables

Several command line options can be passed to dp train, which can be checked with

$ dp train --help

An explanation will be provided

positional arguments:
  INPUT                 the input json database

optional arguments:
  -h, --help            show this help message and exit
 
  --init-model INIT_MODEL
                        Initialize a model by the provided checkpoint

  --restart RESTART     Restart the training from the provided checkpoint
 
  --init-frz-model INIT_FRZ_MODEL
                        Initialize the training from the frozen model.
  --skip-neighbor-stat  Skip calculating neighbor statistics. Sel checking, automatic sel, and model compression will be disabled. (default: False)

--init-model model.ckpt, initializes the model training with an existing model that is stored in the checkpoint model.ckpt, the network architectures should match.

--restart model.ckpt, continues the training from the checkpoint model.ckpt.

--init-frz-model frozen_model.pb, initializes the training with an existing model that is stored in frozen_model.pb.

--skip-neighbor-stat will skip calculating neighbor statistics if one is concerned about performance. Some features will be disabled.

To get the best performance, one should control the number of threads used by DeePMD-kit. This is achieved by three environmental variables: OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS and TF_INTER_OP_PARALLELISM_THREADS. OMP_NUM_THREADS controls the multithreading of DeePMD-kit implemented operations. TF_INTRA_OP_PARALLELISM_THREADS and TF_INTER_OP_PARALLELISM_THREADS controls intra_op_parallelism_threads and inter_op_parallelism_threads, which are Tensorflow configurations for multithreading. An explanation is found here.

For example if you wish to use 3 cores of 2 CPUs on one node, you may set the environmental variables and run DeePMD-kit as follows:

export OMP_NUM_THREADS=3
export TF_INTRA_OP_PARALLELISM_THREADS=3
export TF_INTER_OP_PARALLELISM_THREADS=2
dp train input.json

For a node with 128 cores, it is recommended to start with the following variables:

export OMP_NUM_THREADS=16
export TF_INTRA_OP_PARALLELISM_THREADS=16
export TF_INTER_OP_PARALLELISM_THREADS=8

It is encouraged to adjust the configurations after empirical testing.

One can set other environmental variables:

Environment variables	Allowed value	Default value	Usage
DP_INTERFACE_PREC	`high`, `low`	`high`	Control high (double) or low (float) precision of training.
DP_AUTO_PARALLELIZATION	0, 1	0	Enable auto parallelization for CPU operators.

Adjust `sel` of a frozen model

One can use --init-frz-model features to adjust (increase or decrease) sel of a existing model. Firstly, one need to adjust sel in input.json. For example, adjust from [46, 92] to [23, 46].

"model": {
	"descriptor": {
		"sel": [23, 46]
	}
}

To obtain the new model at once, numb_steps should be set to zero:

"training": {
	"numb_steps": 0
}

Then, one can initialize the training from the frozen model and freeze the new model at once:

dp train input.json --init-frz-model frozen_model.pb
dp freeze -o frozen_model_adjusted_sel.pb

Two models should give the same result when the input satisfies both constraints.

Note: At this time, this feature is only supported by se_e2_a descriptor with set_davg_true enable, or hybrid composed of above descriptors.

Training Parameters

Note

One can load, modify, and export the input file by using our effective web-based tool DP-GUI. All training parameters below can be set in DP-GUI. By clicking “SAVE JSON”, one can download the input file for furthur training.

model:

type: dict

argument path: model

type_map:

type: list, optional

argument path: model/type_map

A list of strings. Give the name to each type of atoms. It is noted that the number of atom type of training system must be less than 128 in a GPU environment.

data_stat_nbatch:

type: int, optional, default: 10

argument path: model/data_stat_nbatch

The model determines the normalization from the statistics of the data. This key specifies the number of frames in each system used for statistics.

data_stat_protect:

type: float, optional, default: 0.01

argument path: model/data_stat_protect

Protect parameter for atomic energy regression.

use_srtab:

type: str, optional

argument path: model/use_srtab

The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.

smin_alpha:

type: float, optional

argument path: model/smin_alpha

The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when use_srtab is provided.

sw_rmin:

type: float, optional

argument path: model/sw_rmin

The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

sw_rmax:

type: float, optional

argument path: model/sw_rmax

The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

type_embedding:

type: dict, optional

argument path: model/type_embedding

The type embedding.

neuron:

type: list, optional, default: [8]

argument path: model/type_embedding/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

activation_function:

type: str, optional, default: tanh

argument path: model/type_embedding/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: False

argument path: model/type_embedding/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:

type: str, optional, default: default

argument path: model/type_embedding/precision

The precision of the embedding net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

trainable:

type: bool, optional, default: True

argument path: model/type_embedding/trainable

If the parameters in the embedding net are trainable

seed:

type: int | NoneType, optional, default: None

argument path: model/type_embedding/seed

Random seed for parameter initialization

descriptor:

type: dict

argument path: model/descriptor

The descriptor of atomic environment.

Depending on the value of type, different sub args are accepted.

type:

type: str (flag key)

argument path: model/descriptor/type

possible choices: loc_frame, se_e2_a, se_e3, se_a_tpe, se_e2_r, hybrid, se_atten

The type of the descritpor. See explanation below.

loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.

se_e2_a: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.

se_e2_r: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.

se_e3: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor.

se_a_tpe: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor.

se_atten: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor.

hybrid: Concatenate of a list of descriptors as a new descriptor.

When type is set to loc_frame:

sel_a:

type: list

argument path: model/descriptor[loc_frame]/sel_a

A list of integers. The length of the list should be the same as the number of atom types in the system. sel_a[i] gives the selected number of type-i neighbors. The full relative coordinates of the neighbors are used by the descriptor.

sel_r:

type: list

argument path: model/descriptor[loc_frame]/sel_r

A list of integers. The length of the list should be the same as the number of atom types in the system. sel_r[i] gives the selected number of type-i neighbors. Only relative distance of the neighbors are used by the descriptor. sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.

rcut:

type: float, optional, default: 6.0

argument path: model/descriptor[loc_frame]/rcut

The cut-off radius. The default value is 6.0

axis_rule:

type: list

argument path: model/descriptor[loc_frame]/axis_rule

A list of integers. The length should be 6 times of the number of types.

axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.

axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.

axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.

axis_rule[i*6+3]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.

axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.

axis_rule[i*6+5]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.

When type is set to se_e2_a (or its alias se_a):

sel:

type: list | str, optional, default: auto

argument path: model/descriptor[se_e2_a]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:

type: float, optional, default: 6.0

argument path: model/descriptor[se_e2_a]/rcut

The cut-off radius.

rcut_smth:

type: float, optional, default: 0.5

argument path: model/descriptor[se_e2_a]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:

type: list, optional, default: [10, 20, 40]

argument path: model/descriptor[se_e2_a]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:

type: int, optional, default: 4, alias: n_axis_neuron

argument path: model/descriptor[se_e2_a]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:

type: str, optional, default: tanh

argument path: model/descriptor[se_e2_a]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: False

argument path: model/descriptor[se_e2_a]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:

type: bool, optional, default: False

argument path: model/descriptor[se_e2_a]/type_one_side

Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets

precision:

type: str, optional, default: default

argument path: model/descriptor[se_e2_a]/precision

The precision of the embedding net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

trainable:

type: bool, optional, default: True

argument path: model/descriptor[se_e2_a]/trainable

If the parameters in the embedding net is trainable

seed:

type: int | NoneType, optional

argument path: model/descriptor[se_e2_a]/seed

Random seed for parameter initialization

exclude_types:

type: list, optional, default: []

argument path: model/descriptor[se_e2_a]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

set_davg_zero:

type: bool, optional, default: False

argument path: model/descriptor[se_e2_a]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

When type is set to se_e3 (or its aliases se_at, se_a_3be, se_t):

sel:

type: list | str, optional, default: auto

argument path: model/descriptor[se_e3]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:

type: float, optional, default: 6.0

argument path: model/descriptor[se_e3]/rcut

The cut-off radius.

rcut_smth:

type: float, optional, default: 0.5

argument path: model/descriptor[se_e3]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:

type: list, optional, default: [10, 20, 40]

argument path: model/descriptor[se_e3]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

activation_function:

type: str, optional, default: tanh

argument path: model/descriptor[se_e3]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: False

argument path: model/descriptor[se_e3]/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:

type: str, optional, default: default

argument path: model/descriptor[se_e3]/precision

The precision of the embedding net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

trainable:

type: bool, optional, default: True

argument path: model/descriptor[se_e3]/trainable

If the parameters in the embedding net are trainable

seed:

type: int | NoneType, optional

argument path: model/descriptor[se_e3]/seed

Random seed for parameter initialization

set_davg_zero:

type: bool, optional, default: False

argument path: model/descriptor[se_e3]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

When type is set to se_a_tpe (or its alias se_a_ebd):

sel:

type: list | str, optional, default: auto

argument path: model/descriptor[se_a_tpe]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:

type: float, optional, default: 6.0

argument path: model/descriptor[se_a_tpe]/rcut

The cut-off radius.

rcut_smth:

type: float, optional, default: 0.5

argument path: model/descriptor[se_a_tpe]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:

type: list, optional, default: [10, 20, 40]

argument path: model/descriptor[se_a_tpe]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:

type: int, optional, default: 4, alias: n_axis_neuron

argument path: model/descriptor[se_a_tpe]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:

type: str, optional, default: tanh

argument path: model/descriptor[se_a_tpe]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: False

argument path: model/descriptor[se_a_tpe]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:

type: bool, optional, default: False

argument path: model/descriptor[se_a_tpe]/type_one_side

Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets

precision:

type: str, optional, default: default

argument path: model/descriptor[se_a_tpe]/precision

The precision of the embedding net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

trainable:

type: bool, optional, default: True

argument path: model/descriptor[se_a_tpe]/trainable

If the parameters in the embedding net is trainable

seed:

type: int | NoneType, optional

argument path: model/descriptor[se_a_tpe]/seed

Random seed for parameter initialization

exclude_types:

type: list, optional, default: []

argument path: model/descriptor[se_a_tpe]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

set_davg_zero:

type: bool, optional, default: False

argument path: model/descriptor[se_a_tpe]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

type_nchanl:

type: int, optional, default: 4

argument path: model/descriptor[se_a_tpe]/type_nchanl

number of channels for type embedding

type_nlayer:

type: int, optional, default: 2

argument path: model/descriptor[se_a_tpe]/type_nlayer

number of hidden layers of type embedding net

numb_aparam:

type: int, optional, default: 0

argument path: model/descriptor[se_a_tpe]/numb_aparam

dimension of atomic parameter. if set to a value > 0, the atomic parameters are embedded.

When type is set to se_e2_r (or its alias se_r):

sel:

type: list | str, optional, default: auto

argument path: model/descriptor[se_e2_r]/sel

This parameter set the number of selected neighbors for each type of atom. It can be:

List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.

str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:

type: float, optional, default: 6.0

argument path: model/descriptor[se_e2_r]/rcut

The cut-off radius.

rcut_smth:

type: float, optional, default: 0.5

argument path: model/descriptor[se_e2_r]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:

type: list, optional, default: [10, 20, 40]

argument path: model/descriptor[se_e2_r]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

activation_function:

type: str, optional, default: tanh

argument path: model/descriptor[se_e2_r]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: False

argument path: model/descriptor[se_e2_r]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:

type: bool, optional, default: False

argument path: model/descriptor[se_e2_r]/type_one_side

Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets

precision:

type: str, optional, default: default

argument path: model/descriptor[se_e2_r]/precision

The precision of the embedding net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

trainable:

type: bool, optional, default: True

argument path: model/descriptor[se_e2_r]/trainable

If the parameters in the embedding net are trainable

seed:

type: int | NoneType, optional

argument path: model/descriptor[se_e2_r]/seed

Random seed for parameter initialization

exclude_types:

type: list, optional, default: []

argument path: model/descriptor[se_e2_r]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

set_davg_zero:

type: bool, optional, default: False

argument path: model/descriptor[se_e2_r]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

When type is set to hybrid:

list:

type: list

argument path: model/descriptor[hybrid]/list

A list of descriptor definitions

When type is set to se_atten:

sel:

type: int | list | str, optional, default: auto

argument path: model/descriptor[se_atten]/sel

This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:

int. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200.

List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. Only the summation of sel[i] matters, and it is recommended to be less than 200. - str. Can be “auto:factor” or “auto”. “factor” is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the “factor”. Finally the number is wraped up to 4 divisible. The option “auto” is equivalent to “auto:1.1”.

rcut:

type: float, optional, default: 6.0

argument path: model/descriptor[se_atten]/rcut

The cut-off radius.

rcut_smth:

type: float, optional, default: 0.5

argument path: model/descriptor[se_atten]/rcut_smth

Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth

neuron:

type: list, optional, default: [10, 20, 40]

argument path: model/descriptor[se_atten]/neuron

Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.

axis_neuron:

type: int, optional, default: 4, alias: n_axis_neuron

argument path: model/descriptor[se_atten]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:

type: str, optional, default: tanh

argument path: model/descriptor[se_atten]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: False

argument path: model/descriptor[se_atten]/resnet_dt

Whether to use a “Timestep” in the skip connection

type_one_side:

type: bool, optional, default: False

argument path: model/descriptor[se_atten]/type_one_side

Whether to consider the information from only one side or both sides.

precision:

type: str, optional, default: default

argument path: model/descriptor[se_atten]/precision

The precision of the embedding net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

trainable:

type: bool, optional, default: True

argument path: model/descriptor[se_atten]/trainable

If the parameters in the embedding net is trainable

seed:

type: int | NoneType, optional

argument path: model/descriptor[se_atten]/seed

Random seed for parameter initialization

exclude_types:

type: list, optional, default: []

argument path: model/descriptor[se_atten]/exclude_types

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

set_davg_zero:

type: bool, optional, default: False

argument path: model/descriptor[se_atten]/set_davg_zero

Set the normalization average to zero. This option should be set when atom_ener in the energy fitting is used

attn:

type: int, optional, default: 128

argument path: model/descriptor[se_atten]/attn

The length of hidden vectors in attention layers

attn_layer:

type: int, optional, default: 2

argument path: model/descriptor[se_atten]/attn_layer

The number of attention layers

attn_dotr:

type: bool, optional, default: True

argument path: model/descriptor[se_atten]/attn_dotr

Whether to do dot product with the normalized relative coordinates

attn_mask:

type: bool, optional, default: False

argument path: model/descriptor[se_atten]/attn_mask

Whether to do mask on the diagonal in the attention matrix

fitting_net:

type: dict

argument path: model/fitting_net

The fitting of physical properties.

Depending on the value of type, different sub args are accepted.

type:

type: str (flag key), default: ener

argument path: model/fitting_net/type

possible choices: ener, dipole, polar

The type of the fitting. See explanation below.

ener: Fit an energy model (potential energy surface).

dipole: Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see sel_type) should be provided by dipole.npy in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See loss parameter.

polar: Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see sel_type) should be provided by polarizability.npy in each data system. The file eith has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See loss parameter.

When type is set to ener:

numb_fparam:

type: int, optional, default: 0

argument path: model/fitting_net[ener]/numb_fparam

The dimension of the frame parameter. If set to >0, file fparam.npy should be included to provided the input fparams.

numb_aparam:

type: int, optional, default: 0

argument path: model/fitting_net[ener]/numb_aparam

The dimension of the atomic parameter. If set to >0, file aparam.npy should be included to provided the input aparams.

neuron:

type: list, optional, default: [120, 120, 120], alias: n_neuron

argument path: model/fitting_net[ener]/neuron

The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.

activation_function:

type: str, optional, default: tanh

argument path: model/fitting_net[ener]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

precision:

type: str, optional, default: default

argument path: model/fitting_net[ener]/precision

The precision of the fitting net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

resnet_dt:

type: bool, optional, default: True

argument path: model/fitting_net[ener]/resnet_dt

Whether to use a “Timestep” in the skip connection

trainable:

type: list | bool, optional, default: True

argument path: model/fitting_net[ener]/trainable

Whether the parameters in the fitting net are trainable. This option can be

bool: True if all parameters of the fitting net are trainable, False otherwise.

list of bool: Specifies if each layer is trainable. Since the fitting net is composed by hidden layers followed by a output layer, the length of tihs list should be equal to len(neuron)+1.

rcond:

type: float, optional, default: 0.001

argument path: model/fitting_net[ener]/rcond

The condition number used to determine the inital energy shift for each type of atoms.

seed:

type: int | NoneType, optional

argument path: model/fitting_net[ener]/seed

Random seed for parameter initialization of the fitting net

atom_ener:

type: list, optional, default: []

argument path: model/fitting_net[ener]/atom_ener

Specify the atomic energy in vacuum for each type

When type is set to dipole:

neuron:

type: list, optional, default: [120, 120, 120], alias: n_neuron

argument path: model/fitting_net[dipole]/neuron

The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.

activation_function:

type: str, optional, default: tanh

argument path: model/fitting_net[dipole]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: True

argument path: model/fitting_net[dipole]/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:

type: str, optional, default: default

argument path: model/fitting_net[dipole]/precision

The precision of the fitting net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

sel_type:

type: list | NoneType | int, optional, alias: dipole_type

argument path: model/fitting_net[dipole]/sel_type

The atom types for which the atomic dipole will be provided. If not set, all types will be selected.

seed:

type: int | NoneType, optional

argument path: model/fitting_net[dipole]/seed

Random seed for parameter initialization of the fitting net

When type is set to polar:

neuron:

type: list, optional, default: [120, 120, 120], alias: n_neuron

argument path: model/fitting_net[polar]/neuron

The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.

activation_function:

type: str, optional, default: tanh

argument path: model/fitting_net[polar]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”. Note that “gelu” denotes the custom operator version, and “gelu_tf” denotes the TF standard version.

resnet_dt:

type: bool, optional, default: True

argument path: model/fitting_net[polar]/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:

type: str, optional, default: default

argument path: model/fitting_net[polar]/precision

The precision of the fitting net parameters, supported options are “default”, “float16”, “float32”, “float64”. Default follows the interface precision.

fit_diag:

type: bool, optional, default: True

argument path: model/fitting_net[polar]/fit_diag

Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.

scale:

type: list | float, optional, default: 1.0

argument path: model/fitting_net[polar]/scale

The output of the fitting net (polarizability matrix) will be scaled by scale

shift_diag:

type: bool, optional, default: True

argument path: model/fitting_net[polar]/shift_diag

Whether to shift the diagonal of polar, which is beneficial to training. Default is true.

sel_type:

type: list | NoneType | int, optional, alias: pol_type

argument path: model/fitting_net[polar]/sel_type

The atom types for which the atomic polarizability will be provided. If not set, all types will be selected.

seed:

type: int | NoneType, optional

argument path: model/fitting_net[polar]/seed

Random seed for parameter initialization of the fitting net

modifier:

type: dict, optional

argument path: model/modifier

The modifier of model output.

Depending on the value of type, different sub args are accepted.

type:

type: str (flag key)

argument path: model/modifier/type

possible choices: dipole_charge

The type of modifier. See explanation below.

-dipole_charge: Use WFCC to model the electronic structure of the system. Correct the long-range interaction

When type is set to dipole_charge:

model_name:

type: str

argument path: model/modifier[dipole_charge]/model_name

The name of the frozen dipole model file.

model_charge_map:

type: list

argument path: model/modifier[dipole_charge]/model_charge_map

The charge of the WFCC. The list length should be the same as the sel_type.

sys_charge_map:

type: list

argument path: model/modifier[dipole_charge]/sys_charge_map

The charge of real atoms. The list length should be the same as the type_map

ewald_beta:

type: float, optional, default: 0.4

argument path: model/modifier[dipole_charge]/ewald_beta

The splitting parameter of Ewald sum. Unit is A^-1

ewald_h:

type: float, optional, default: 1.0

argument path: model/modifier[dipole_charge]/ewald_h

The grid spacing of the FFT grid. Unit is A

compress:

type: dict, optional

argument path: model/compress

Model compression configurations

Depending on the value of type, different sub args are accepted.

type:

type: str (flag key), default: se_e2_a

argument path: model/compress/type

possible choices: se_e2_a

The type of model compression, which should be consistent with the descriptor type.

When type is set to se_e2_a (or its alias se_a):

model_file:

type: str

argument path: model/compress[se_e2_a]/model_file

The input model file, which will be compressed by the DeePMD-kit.

table_config:

type: list

argument path: model/compress[se_e2_a]/table_config

The arguments of model compression, including extrapolate(scale of model extrapolation), stride(uniform stride of tabulation’s first and second table), and frequency(frequency of tabulation overflow check).

min_nbor_dist:

type: float

argument path: model/compress[se_e2_a]/min_nbor_dist

The nearest distance between neighbor atoms saved in the frozen model.

learning_rate:

type: dict

argument path: learning_rate

The definitio of learning rate

scale_by_worker:

type: str, optional, default: linear

argument path: learning_rate/scale_by_worker

When parallel training or batch size scaled, how to alter learning rate. Valid values are linear`(default), `sqrt or none.

Depending on the value of type, different sub args are accepted.

type:

type: str (flag key), default: exp

argument path: learning_rate/type

possible choices: exp

The type of the learning rate.

When type is set to exp:

start_lr:

type: float, optional, default: 0.001

argument path: learning_rate[exp]/start_lr

The learning rate the start of the training.

stop_lr:

type: float, optional, default: 1e-08

argument path: learning_rate[exp]/stop_lr

The desired learning rate at the end of the training.

decay_steps:

type: int, optional, default: 5000

argument path: learning_rate[exp]/decay_steps

The learning rate is decaying every this number of training steps.

loss:

type: dict, optional

argument path: loss

The definition of loss function. The loss type should be set to tensor, ener or left unset. .

Depending on the value of type, different sub args are accepted.

type:

type: str (flag key), default: ener

argument path: loss/type

possible choices: ener, tensor

The type of the loss. When the fitting type is ener, the loss type should be set to ener or left unset. When the fitting type is dipole or polar, the loss type should be set to tensor. .

When type is set to ener:

start_pref_e:

type: int | float, optional, default: 0.02

argument path: loss[ener]/start_pref_e

The prefactor of energy loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the energy label should be provided by file energy.npy in each data system. If both start_pref_energy and limit_pref_energy are set to 0, then the energy will be ignored.

limit_pref_e:

type: int | float, optional, default: 1.0

argument path: loss[ener]/limit_pref_e

The prefactor of energy loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_f:

type: int | float, optional, default: 1000

argument path: loss[ener]/start_pref_f

The prefactor of force loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the force label should be provided by file force.npy in each data system. If both start_pref_force and limit_pref_force are set to 0, then the force will be ignored.

limit_pref_f:

type: int | float, optional, default: 1.0

argument path: loss[ener]/limit_pref_f

The prefactor of force loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_v:

type: int | float, optional, default: 0.0

argument path: loss[ener]/start_pref_v

The prefactor of virial loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the virial label should be provided by file virial.npy in each data system. If both start_pref_virial and limit_pref_virial are set to 0, then the virial will be ignored.

limit_pref_v:

type: int | float, optional, default: 0.0

argument path: loss[ener]/limit_pref_v

The prefactor of virial loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_ae:

type: int | float, optional, default: 0.0

argument path: loss[ener]/start_pref_ae

The prefactor of atom_ener loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atom_ener label should be provided by file atom_ener.npy in each data system. If both start_pref_atom_ener and limit_pref_atom_ener are set to 0, then the atom_ener will be ignored.

limit_pref_ae:

type: int | float, optional, default: 0.0

argument path: loss[ener]/limit_pref_ae

The prefactor of atom_ener loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

start_pref_pf:

type: int | float, optional, default: 0.0

argument path: loss[ener]/start_pref_pf

The prefactor of atom_pref loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atom_pref label should be provided by file atom_pref.npy in each data system. If both start_pref_atom_pref and limit_pref_atom_pref are set to 0, then the atom_pref will be ignored.

limit_pref_pf:

type: int | float, optional, default: 0.0

argument path: loss[ener]/limit_pref_pf

The prefactor of atom_pref loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.

relative_f:

type: NoneType | float, optional

argument path: loss[ener]/relative_f

If provided, relative force error will be used in the loss. The difference of force will be normalized by the magnitude of the force in the label with a shift given by relative_f, i.e. DF_i / ( || F || + relative_f ) with DF denoting the difference between prediction and label and || F || denoting the L2 norm of the label.

enable_atom_ener_coeff:

type: bool, optional, default: False

argument path: loss[ener]/enable_atom_ener_coeff

If true, the energy will be computed as sum_i c_i E_i. c_i should be provided by file atom_ener_coeff.npy in each data system, otherwise it’s 1.

When type is set to tensor:

pref:

type: int | float

argument path: loss[tensor]/pref

The prefactor of the weight of global loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to global label, i.e. ‘polarizability.npy` or dipole.npy, whose shape should be #frames x [9 or 3]. If it’s larger than 0.0, this npy should be included.

pref_atomic:

type: int | float

argument path: loss[tensor]/pref_atomic

The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to atomic label, i.e. atomic_polarizability.npy or atomic_dipole.npy, whose shape should be #frames x ([9 or 3] x #selected atoms). If it’s larger than 0.0, this npy should be included. Both pref and pref_atomic should be provided, and either can be set to 0.0.

training:

type: dict

argument path: training

The training options.

training_data:

type: dict

argument path: training/training_data

Configurations of training data.

systems:

type: list | str

argument path: training/training_data/systems

The data systems for training. This key can be provided with a list that specifies the systems, or be provided with a string by which the prefix of all systems are given and the list of the systems is automatically generated.

set_prefix:

type: str, optional, default: set

argument path: training/training_data/set_prefix

The prefix of the sets in the systems.

batch_size:

type: list | int | str, optional, default: auto

argument path: training/training_data/batch_size

This key can be

list: the length of which is the same as the systems. The batch size of each system is given by the elements of the list.

int: all systems use the same batch size.

string “auto”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.

string “auto:N”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.

auto_prob:

type: str, optional, default: prob_sys_size, alias: auto_prob_style

argument path: training/training_data/auto_prob

Determine the probability of systems automatically. The method is assigned by this key and can be

“prob_uniform” : the probability all the systems are equal, namely 1.0/self.get_nsystems()

“prob_sys_size” : the probability of a system is proportional to the number of batches in the system

“prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;…” : the list of systems is devided into blocks. A block is specified by stt_idx:end_idx:weight, where stt_idx is the starting index of the system, end_idx is then ending (not including) index of the system, the probabilities of the systems in this block sums up to weight, and the relatively probabilities within this block is proportional to the number of batches in the system.

sys_probs:

type: list | NoneType, optional, default: None, alias: sys_weights

argument path: training/training_data/sys_probs

A list of float if specified. Should be of the same length as systems, specifying the probability of each system.

validation_data:

type: NoneType | dict, optional, default: None

argument path: training/validation_data

Configurations of validation data. Similar to that of training data, except that a numb_btch argument may be configured

systems:

type: list | str

argument path: training/validation_data/systems

The data systems for validation. This key can be provided with a list that specifies the systems, or be provided with a string by which the prefix of all systems are given and the list of the systems is automatically generated.

set_prefix:

type: str, optional, default: set

argument path: training/validation_data/set_prefix

The prefix of the sets in the systems.

batch_size:

type: list | int | str, optional, default: auto

argument path: training/validation_data/batch_size

This key can be

list: the length of which is the same as the systems. The batch size of each system is given by the elements of the list.

int: all systems use the same batch size.

string “auto”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.

string “auto:N”: automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.

auto_prob:

type: str, optional, default: prob_sys_size, alias: auto_prob_style

argument path: training/validation_data/auto_prob

Determine the probability of systems automatically. The method is assigned by this key and can be

“prob_uniform” : the probability all the systems are equal, namely 1.0/self.get_nsystems()

“prob_sys_size” : the probability of a system is proportional to the number of batches in the system

“prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;…” : the list of systems is devided into blocks. A block is specified by stt_idx:end_idx:weight, where stt_idx is the starting index of the system, end_idx is then ending (not including) index of the system, the probabilities of the systems in this block sums up to weight, and the relatively probabilities within this block is proportional to the number of batches in the system.

sys_probs:

type: list | NoneType, optional, default: None, alias: sys_weights

argument path: training/validation_data/sys_probs

A list of float if specified. Should be of the same length as systems, specifying the probability of each system.

numb_btch:

type: int, optional, default: 1, alias: numb_batch

argument path: training/validation_data/numb_btch

An integer that specifies the number of systems to be sampled for each validation period.

mixed_precision:

type: dict, optional

argument path: training/mixed_precision

Configurations of mixed precision.

output_prec:

type: str, optional, default: float32

argument path: training/mixed_precision/output_prec

The precision for mixed precision params. ” “The trainable variables precision during the mixed precision training process, ” “supported options are float32 only currently.

compute_prec:

type: str

argument path: training/mixed_precision/compute_prec

The precision for mixed precision compute. ” “The compute precision during the mixed precision training process, “” “supported options are float16 only currently.

numb_steps:

type: int, alias: stop_batch

argument path: training/numb_steps

Number of training batch. Each training uses one batch of data.

seed:

type: int | NoneType, optional

argument path: training/seed

The random seed for getting frames from the training data set.

disp_file:

type: str, optional, default: lcurve.out

argument path: training/disp_file

The file for printing learning curve.

disp_freq:

type: int, optional, default: 1000

argument path: training/disp_freq

The frequency of printing learning curve.

save_freq:

type: int, optional, default: 1000

argument path: training/save_freq

The frequency of saving check point.

save_ckpt:

type: str, optional, default: model.ckpt

argument path: training/save_ckpt

The file name of saving check point.

disp_training:

type: bool, optional, default: True

argument path: training/disp_training

Displaying verbose information during training.

time_training:

type: bool, optional, default: True

argument path: training/time_training

Timing durining training.

profiling:

type: bool, optional, default: False

argument path: training/profiling

Profiling during training.

profiling_file:

type: str, optional, default: timeline.json

argument path: training/profiling_file

Output file for profiling.

enable_profiler:

type: bool, optional, default: False

argument path: training/enable_profiler

Enable TensorFlow Profiler (available in TensorFlow 2.3) to analyze performance. The log will be saved to tensorboard_log_dir.

tensorboard:

type: bool, optional, default: False

argument path: training/tensorboard

Enable tensorboard

tensorboard_log_dir:

type: str, optional, default: log

argument path: training/tensorboard_log_dir

The log directory of tensorboard outputs

tensorboard_freq:

type: int, optional, default: 1

argument path: training/tensorboard_freq

The frequency of writing tensorboard events.

nvnmd:

type: dict, optional

argument path: nvnmd

The nvnmd options.

net_size:

type: int

argument path: nvnmd/net_size

configuration the number of nodes of fitting_net, just can be set as 128

map_file:

type: str

argument path: nvnmd/map_file

A file containing the mapping tables to replace the calculation of embedding nets

config_file:

type: str

argument path: nvnmd/config_file

A file containing the parameters about how to implement the model in certain hardware

weight_file:

type: str

argument path: nvnmd/weight_file

a *.npy file containing the weights of the model

enable:

type: bool

argument path: nvnmd/enable

enable the nvnmd training

restore_descriptor:

type: bool

argument path: nvnmd/restore_descriptor

enable to restore the parameter of embedding_net from weight.npy

restore_fitting_net:

type: bool

argument path: nvnmd/restore_fitting_net

enable to restore the parameter of fitting_net from weight.npy

quantize_descriptor:

type: bool

argument path: nvnmd/quantize_descriptor

enable the quantizatioin of descriptor

quantize_fitting_net:

type: bool

argument path: nvnmd/quantize_fitting_net

enable the quantizatioin of fitting_net

Parallel training

Currently, parallel training is enabled in a sychoronized way with help of Horovod. Depend on the number of training processes (according to MPI context) and number of GPU cards avaliable, DeePMD-kit will decide whether to launch the training in parallel (distributed) mode or in serial mode. Therefore, no additional options is specified in your JSON/YAML input file.

Tuning learning rate

Horovod works in the data-parallel mode, resulting in a larger global batch size. For example, the real batch size is 8 when batch_size is set to 2 in the input file and you launch 4 workers. Thus, learning_rate is automatically scaled by the number of workers for better convergence. Technical details of such heuristic rule are discussed at Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

The number of decay steps required to achieve same accuracy can decrease by the number of cards (e.g., 1/2 of steps in the above case), but needs to be scaled manually in the input file.

In some cases, it won’t work well when scale learning rate by worker count in a linear way. Then you can try sqrt or none by setting argument scale_by_worker like below.

    "learning_rate" :{
        "scale_by_worker": "none",
        "type": "exp"
    }

Scaling test

Testing examples/water/se_e2_a on a 8-GPU host, linear acceleration can be observed with increasing number of cards.

Num of GPU cards	Seconds every 100 samples	Samples per second	Speed up
1	1.4515	68.89	1.00
2	1.5962	62.65*2	1.82
4	1.7635	56.71*4	3.29
8	1.7267	57.91*8	6.72

How to use

Training workers can be launched with horovodrun. The following command launches 4 processes on the same host:

CUDA_VISIBLE_DEVICES=4,5,6,7 horovodrun -np 4 \
    dp train --mpi-log=workers input.json

Need to mention, environment variable CUDA_VISIBLE_DEVICES must be set to control parallelism on the occupied host where one process is bound to one GPU card.

Note that OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS should be carefully adjusted to achieve the best performance.

When using MPI with Horovod, horovodrun is a simple wrapper around mpirun. In the case where fine-grained control over options passed to mpirun, mpirun can be invoked directly, and it will be detected automatically by Horovod, e.g.,

CUDA_VISIBLE_DEVICES=4,5,6,7 mpirun -l -launcher=fork -hosts=localhost -np 4 \
    dp train --mpi-log=workers input.json

this is sometimes neccessary on HPC environment.

Whether distributed workers are initiated can be observed at the “Summary of the training” section in the log (world size > 1, and distributed).

[0] DEEPMD INFO    ---Summary of the training---------------------------------------
[0] DEEPMD INFO    distributed
[0] DEEPMD INFO    world size:           4
[0] DEEPMD INFO    my rank:              0
[0] DEEPMD INFO    node list:            ['exp-13-57']
[0] DEEPMD INFO    running on:           exp-13-57
[0] DEEPMD INFO    computing device:     gpu:0
[0] DEEPMD INFO    CUDA_VISIBLE_DEVICES: 0,1,2,3
[0] DEEPMD INFO    Count of visible GPU: 4
[0] DEEPMD INFO    num_intra_threads:    0
[0] DEEPMD INFO    num_inter_threads:    0
[0] DEEPMD INFO    -----------------------------------------------------------------

Logging

What’s more, 2 command-line arguments are defined to control the logging behvaior when performing parallel training with MPI.

optional arguments:
  -l LOG_PATH, --log-path LOG_PATH
                        set log file to log messages to disk, if not
                        specified, the logs will only be output to console
                        (default: None)
  -m {master,collect,workers}, --mpi-log {master,collect,workers}
                        Set the manner of logging when running with MPI.
                        'master' logs only on main process, 'collect'
                        broadcasts logs from workers to master and 'workers'
                        means each process will output its own log (default:
                        master)

TensorBoard Usage

TensorBoard provides the visualization and tooling needed for machine learning experimentation. A full instruction of tensorboard can be found here.

Highlighted features

DeePMD-kit can now use most of the interesting features enabled by tensorboard!

Tracking and visualizing metrics, such as l2_loss, l2_energy_loss and l2_force_loss
Visualizing the model graph (ops and layers)
Viewing histograms of weights, biases, or other tensors as they change over time.
Viewing summaries of trainable viriables

How to use Tensorboard with DeePMD-kit

Before running TensorBoard, make sure you have generated summary data in a log directory by modifying the the input script, set tensorboard to true in training subsection will enable the tensorboard data analysis. eg. water_se_a.json.

    "training" : {
	"systems":	["../data/"],
	"set_prefix":	"set",    
	"stop_batch":	1000000,
	"batch_size":	1,

	"seed":		1,

	"_comment": " display and restart",
	"_comment": " frequencies counted in batch",
	"disp_file":	"lcurve.out",
	"disp_freq":	100,
	"numb_test":	10,
	"save_freq":	1000,
	"save_ckpt":	"model.ckpt",

	"disp_training":true,
	"time_training":true,
	"tensorboard":	true,
	"tensorboard_log_dir":"log",
	"tensorboard_freq": 1000,
	"profiling":	false,
	"profiling_file":"timeline.json",
	"_comment":	"that's all"
    }

Once you have event files, run TensorBoard and provide the log directory. This should print that TensorBoard has started. Next, connect to http://tensorboard_server_ip:6006.

TensorBoard requires a logdir to read logs from. For info on configuring TensorBoard, run tensorboard –help. One can easily change the log name with “tensorboard_log_dir” and the sampling frequency with “tensorboard_freq”.

tensorboard --logdir path/to/logs

Examples

Tracking and visualizing loss metrics(red:train, blue:test)

ALT

Visualizing deepmd-kit model graph

ALT

Viewing histograms of weights, biases, or other tensors as they change over time

ALT

Viewing summaries of trainable variables

ALT

Attention

Allowing the tensorboard analysis will takes extra execution time.(eg, 15% increasing @Nvidia GTX 1080Ti double precision with default water sample)

TensorBoard can be used in Google Chrome or Firefox. Other browsers might work, but there may be bugs or performance issues.

Known limitations of using GPUs

If you use deepmd-kit in a GPU environment, the acceptable value range of some variables are additionally restricted compared to the CPU environment due to the software’s GPU implementations:

The number of atom type of a given system must be less than 128.
The maximum distance between an atom and it’s neighbors must be less than 128. It can be controlled by setting the rcut value of training parameters.
Theoretically, the maximum number of atoms that a single GPU can accept is about 10,000,000. However, this value is actually limited by the GPU memory size currently, usually within 1000,000 atoms even at the model compression mode.
The total sel value of training parameters(in model/descriptor section) must be less than 4096.
The size of the last layer of embedding net must be less than 1024 during the model compression process.

Freeze and Compress

Freeze a model

The trained neural network is extracted from a checkpoint and dumped into a database. This process is called “freezing” a model. The idea and part of our code are from Morgan. To freeze a model, typically one does

$ dp freeze -o graph.pb

in the folder where the model is trained. The output database is called graph.pb.

Compress a model

Once the frozen model is obtained from deepmd-kit, we can get the neural network structure and its parameters (weights, biases, etc.) from the trained model, and compress it in the following way:

dp compress -i graph.pb -o graph-compress.pb

where -i gives the original frozen model, -o gives the compressed model. Several other command line options can be passed to dp compress, which can be checked with

$ dp compress --help

An explanation will be provided

usage: dp compress [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
                   [-m {master,collect,workers}] [-i INPUT] [-o OUTPUT]
                   [-s STEP] [-e EXTRAPOLATE] [-f FREQUENCY]
                   [-c CHECKPOINT_FOLDER]

optional arguments:
  -h, --help            show this help message and exit
  -v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}, --log-level {DEBUG,3,INFO,2,WARNING,1,ERROR,0}
                        set verbosity level by string or number, 0=ERROR,
                        1=WARNING, 2=INFO and 3=DEBUG (default: INFO)
  -l LOG_PATH, --log-path LOG_PATH
                        set log file to log messages to disk, if not
                        specified, the logs will only be output to console
                        (default: None)
  -m {master,collect,workers}, --mpi-log {master,collect,workers}
                        Set the manner of logging when running with MPI.
                        'master' logs only on main process, 'collect'
                        broadcasts logs from workers to master and 'workers'
                        means each process will output its own log (default:
                        master)
  -i INPUT, --input INPUT
                        The original frozen model, which will be compressed by
                        the code (default: frozen_model.pb)
  -o OUTPUT, --output OUTPUT
                        The compressed model (default:
                        frozen_model_compressed.pb)
  -s STEP, --step STEP  Model compression uses fifth-order polynomials to
                        interpolate the embedding-net. It introduces two
                        tables with different step size to store the
                        parameters of the polynomials. The first table covers
                        the range of the training data, while the second table
                        is an extrapolation of the training data. The domain
                        of each table is uniformly divided by a given step
                        size. And the step(parameter) denotes the step size of
                        the first table and the second table will use 10 *
                        step as it's step size to save the memory. Usually the
                        value ranges from 0.1 to 0.001. Smaller step means
                        higher accuracy and bigger model size (default: 0.01)
  -e EXTRAPOLATE, --extrapolate EXTRAPOLATE
                        The domain range of the first table is automatically
                        detected by the code: [d_low, d_up]. While the second
                        table ranges from the first table's upper
                        boundary(d_up) to the extrapolate(parameter) * d_up:
                        [d_up, extrapolate * d_up] (default: 5)
  -f FREQUENCY, --frequency FREQUENCY
                        The frequency of tabulation overflow check(Whether the
                        input environment matrix overflow the first or second
                        table range). By default do not check the overflow
                        (default: -1)
  -c CHECKPOINT_FOLDER, --checkpoint-folder CHECKPOINT_FOLDER
                        path to checkpoint folder (default: .)
  -t TRAINING_SCRIPT, --training-script TRAINING_SCRIPT
                        The training script of the input frozen model
                        (default: None)

Parameter explanation

Model compression, which including tabulating the embedding-net. The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. For model descriptor with se_e2_a type, the first sub-table takes the stride(parameter) as it’s uniform stride, while the second sub-table takes 10 * stride as it’s uniform stride; For model descriptor with se_e3 type, the first sub-table takes 10 * stride as it’s uniform stride, while the second sub-table takes 100 * stride as it’s uniform stride. The range of the first table is automatically detected by deepmd-kit, while the second table ranges from the first table’s upper boundary(upper) to the extrapolate(parameter) * upper. Finally, we added a check frequency parameter. It indicates how often the program checks for overflow(if the input environment matrix overflow the first or second table range) during the MD inference.

Justification of model compression

Model compression, with little loss of accuracy, can greatly speed up MD inference time. According to different simulation systems and training parameters, the speedup can reach more than 10 times at both CPU and GPU devices. At the same time, model compression can greatly change the memory usage, reducing as much as 20 times under the same hardware conditions.

Acceptable original model version

The model compression interface requires the version of deepmd-kit used in original model generation should be 2.0.0-alpha.0 or above. If one has a frozen 1.2 or 1.3 model, one can upgrade it through the dp convert-from interface.(eg: dp convert-from 1.2/1.3 -i old_frozen_model.pb -o new_frozen_model.pb)

Acceptable descriptor type

Descriptors with se_e2_a,se_e3, se_e2_r type are supported by the model compression feature. Hybrid mixed with above descriptors is also supported.

Available activation functions for descriptor:

tanh
gelu
relu
relu6
softplus
sigmoid

Test

Test a model

The frozen model can be used in many ways. The most straightforward test can be performed using dp test. A typical usage of dp test is

dp test -m graph.pb -s /path/to/system -n 30

where -m gives the tested model, -s the path to the tested system and -n the number of tested frames. Several other command line options can be passed to dp test, which can be checked with

$ dp test --help

An explanation will be provided

usage: dp test [-h] [-m MODEL] [-s SYSTEM] [-S SET_PREFIX] [-n NUMB_TEST]
               [-r RAND_SEED] [--shuffle-test] [-d DETAIL_FILE]

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Frozen model file to import
  -s SYSTEM, --system SYSTEM
                        The system dir
  -S SET_PREFIX, --set-prefix SET_PREFIX
                        The set prefix
  -n NUMB_TEST, --numb-test NUMB_TEST
                        The number of data for test
  -r RAND_SEED, --rand-seed RAND_SEED
                        The random seed
  --shuffle-test        Shuffle test data
  -d DETAIL_FILE, --detail-file DETAIL_FILE
                        The file containing details of energy force and virial
                        accuracy

Calculate Model Deviation

One can also use a subcommand to calculate deviation of prediced forces or virials for a bunch of models in the following way:

dp model-devi -m graph.000.pb graph.001.pb graph.002.pb graph.003.pb -s ./data -o model_devi.out

where -m specifies graph files to be calculated, -s gives the data to be evaluated, -o the file to which model deviation results is dumped. Here is more information on this sub-command:

usage: dp model-devi [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}]
                     [-l LOG_PATH] [-m MODELS [MODELS ...]] [-s SYSTEM]
                     [-S SET_PREFIX] [-o OUTPUT] [-f FREQUENCY] [-i ITEMS]

optional arguments:
  -h, --help            show this help message and exit
  -v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}, --log-level {DEBUG,3,INFO,2,WARNING,1,ERROR,0}
                        set verbosity level by string or number, 0=ERROR,
                        1=WARNING, 2=INFO and 3=DEBUG (default: INFO)
  -l LOG_PATH, --log-path LOG_PATH
                        set log file to log messages to disk, if not
                        specified, the logs will only be output to console
                        (default: None)
  -m MODELS [MODELS ...], --models MODELS [MODELS ...]
                        Frozen models file to import (default:
                        ['graph.000.pb', 'graph.001.pb', 'graph.002.pb',
                        'graph.003.pb'])
  -s SYSTEM, --system SYSTEM
                        The system directory, not support recursive detection.
                        (default: .)
  -S SET_PREFIX, --set-prefix SET_PREFIX
                        The set prefix (default: set)
  -o OUTPUT, --output OUTPUT
                        The output file for results of model deviation
                        (default: model_devi.out)
  -f FREQUENCY, --frequency FREQUENCY
                        The trajectory frequency of the system (default: 1)

For more details with respect to definition of model deviation and its application, please refer to Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, and Weinan E, DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models, Computer Physics Communications, 2020, 253, 107206.

Inference

Note that the model for inference is required to be compatible with the DeePMD-kit package. See Model compatibility for details.

Python interface

One may use the python interface of DeePMD-kit for model inference, an example is given as follows

from deepmd.infer import DeepPot
import numpy as np
dp = DeepPot('graph.pb')
coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1,0,1]
e, f, v = dp.eval(coord, cell, atype)

where e, f and v are predicted energy, force and virial of the system, respectively.

Furthermore, one can use the python interface to calulate model deviation.

from deepmd.infer import calc_model_devi
from deepmd.infer import DeepPot as DP
import numpy as np

coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
cell = np.diag(10 * np.ones(3)).reshape([1, -1])
atype = [1,0,1]
graphs = [DP("graph.000.pb"), DP("graph.001.pb")]
model_devi = calc_model_devi(coord, cell, atype, graphs)

C++ interface

The C++ interface of DeePMD-kit is also avaiable for model interface, which is considered faster than Python interface. An example infer_water.cpp is given below:

#include "deepmd/DeepPot.h"

int main(){
  deepmd::DeepPot dp ("graph.pb");
  std::vector<double > coord = {1., 0., 0., 0., 0., 1.5, 1. ,0. ,3.};
  std::vector<double > cell = {10., 0., 0., 0., 10., 0., 0., 0., 10.};
  std::vector<int > atype = {1, 0, 1};
  double e;
  std::vector<double > f, v;
  dp.compute (e, f, v, coord, atype, cell);
}

where e, f and v are predicted energy, force and virial of the system, respectively.

You can compile infer_water.cpp using gcc:

gcc infer_water.cpp -D HIGH_PREC -L $deepmd_root/lib -L $tensorflow_root/lib -I $deepmd_root/include -Wl,--no-as-needed -ldeepmd_cc -lstdc++ -ltensorflow_cc -Wl,-rpath=$deepmd_root/lib -Wl,-rpath=$tensorflow_root/lib -o infer_water

and then run the program:

./infer_water

Command line interface

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

usage: dp [-h] [--version]
          {config,transfer,train,freeze,test,compress,doc-train-input,model-devi,convert-from,neighbor-stat,train-nvnmd}
          ...

Named Arguments

--version: show program’s version number and exit

Valid subcommands

command: Possible choices: config, transfer, train, freeze, test, compress, doc-train-input, model-devi, convert-from, neighbor-stat, train-nvnmd

Sub-commands:

config

fast configuration of parameter file for smooth model

dp config [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
          [-o OUTPUT]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-o, --output

the output json file

Default: “input.json”

transfer

pass parameters to another model

dp transfer [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
            [-r RAW_MODEL] [-O OLD_MODEL] [-o OUTPUT]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-r, --raw-model

the model receiving parameters

Default: “raw_frozen_model.pb”

-O, --old-model

the model providing parameters

Default: “old_frozen_model.pb”

-o, --output

the model after passing parameters

Default: “frozen_model.pb”

train

train a model

dp train [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
         [-m {master,collect,workers}] [-i INIT_MODEL] [-r RESTART]
         [-o OUTPUT] [-f INIT_FRZ_MODEL] [--skip-neighbor-stat]
         INPUT

Positional Arguments

INPUT: the input parameter file in json or yaml format

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-m, --mpi-log

Possible choices: master, collect, workers

Set the manner of logging when running with MPI. ‘master’ logs only on main process, ‘collect’ broadcasts logs from workers to master and ‘workers’ means each process will output its own log

Default: “master”

-i, --init-model

Initialize the model by the provided checkpoint.

-r, --restart

Restart the training from the provided checkpoint.

-o, --output

The output file of the parameters used in training.

Default: “out.json”

-f, --init-frz-model

Initialize the training from the frozen model.

--skip-neighbor-stat

Skip calculating neighbor statistics. Sel checking, automatic sel, and model compression will be disabled.

Default: False

examples:: dp train input.json dp train input.json –restart model.ckpt dp train input.json –init-model model.ckpt

freeze

freeze the model

dp freeze [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
          [-c CHECKPOINT_FOLDER] [-o OUTPUT] [-n NODE_NAMES] [-w NVNMD_WEIGHT]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-c, --checkpoint-folder

path to checkpoint folder

Default: “.”

-o, --output

name of graph, will output to the checkpoint folder

Default: “frozen_model.pb”

-n, --node-names

the frozen nodes, if not set, determined from the model type

-w, --nvnmd-weight

the name of weight file (.npy), if set, save the model’s weight into the file

examples:: dp freeze dp freeze -o graph.pb

test

test the model

dp test [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH] [-m MODEL]
        [-s SYSTEM] [-S SET_PREFIX] [-n NUMB_TEST] [-r RAND_SEED]
        [--shuffle-test] [-d DETAIL_FILE] [-a]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-m, --model

Frozen model file to import

Default: “frozen_model.pb”

-s, --system

The system dir. Recursively detect systems in this directory

Default: “.”

-S, --set-prefix

The set prefix

Default: “set”

-n, --numb-test

The number of data for test

Default: 100

-r, --rand-seed

The random seed

--shuffle-test

Shuffle test data

Default: False

-d, --detail-file

File where details of energy force and virial accuracy will be written

-a, --atomic

Test the accuracy of atomic label, i.e. energy / tensor (dipole, polar)

Default: False

examples:: dp test -m graph.pb -s /path/to/system -n 30

compress

compress a model

dp compress [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
            [-m {master,collect,workers}] [-i INPUT] [-o OUTPUT] [-s STEP]
            [-e EXTRAPOLATE] [-f FREQUENCY] [-c CHECKPOINT_FOLDER]
            [-t TRAINING_SCRIPT]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-m, --mpi-log

Possible choices: master, collect, workers

Set the manner of logging when running with MPI. ‘master’ logs only on main process, ‘collect’ broadcasts logs from workers to master and ‘workers’ means each process will output its own log

Default: “master”

-i, --input

The original frozen model, which will be compressed by the code

Default: “frozen_model.pb”

-o, --output

The compressed model

Default: “frozen_model_compressed.pb”

-s, --step

Model compression uses fifth-order polynomials to interpolate the embedding-net. It introduces two tables with different step size to store the parameters of the polynomials. The first table covers the range of the training data, while the second table is an extrapolation of the training data. The domain of each table is uniformly divided by a given step size. And the step(parameter) denotes the step size of the first table and the second table will use 10 * step as it’s step size to save the memory. Usually the value ranges from 0.1 to 0.001. Smaller step means higher accuracy and bigger model size

Default: 0.01

-e, --extrapolate

The domain range of the first table is automatically detected by the code: [d_low, d_up]. While the second table ranges from the first table’s upper boundary(d_up) to the extrapolate(parameter) * d_up: [d_up, extrapolate * d_up]

Default: 5

-f, --frequency

The frequency of tabulation overflow check(Whether the input environment matrix overflow the first or second table range). By default do not check the overflow

Default: -1

-c, --checkpoint-folder

path to checkpoint folder

Default: “model-compression”

-t, --training-script

The training script of the input frozen model

examples:: dp compress dp compress -i graph.pb -o compressed.pb

doc-train-input

print the documentation (in rst format) of input training parameters.

dp doc-train-input [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
                   [--out-type OUT_TYPE]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

--out-type

The output type

Default: “rst”

model-devi

calculate model deviation

dp model-devi [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
              [-m MODELS [MODELS ...]] [-s SYSTEM] [-S SET_PREFIX] [-o OUTPUT]
              [-f FREQUENCY]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-m, --models

Frozen models file to import

Default: [‘graph.000.pb’, ‘graph.001.pb’, ‘graph.002.pb’, ‘graph.003.pb’]

-s, --system

The system directory. Recursively detect systems in this directory.

Default: “.”

-S, --set-prefix

The set prefix

Default: “set”

-o, --output

The output file for results of model deviation

Default: “model_devi.out”

-f, --frequency

The trajectory frequency of the system

Default: 1

examples:: dp model-devi -m graph.000.pb graph.001.pb graph.002.pb graph.003.pb -s ./data -o model_devi.out

convert-from

convert lower model version to supported version

dp convert-from [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
                [-i INPUT_MODEL] [-o OUTPUT_MODEL]
                {0.12,1.0,1.1,1.2,1.3,2.0}

Positional Arguments

FROM

Possible choices: 0.12, 1.0, 1.1, 1.2, 1.3, 2.0

The original model compatibility

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-i, --input-model

the input model

Default: “frozen_model.pb”

-o, --output-model

the output model

Default: “convert_out.pb”

examples:: dp convert-from 1.0 -i graph.pb -o graph_new.pb

neighbor-stat

Calculate neighbor statistics

dp neighbor-stat [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
                 [-s SYSTEM] -r RCUT -t TYPE_MAP [TYPE_MAP ...] [--one-type]

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-s, --system

The system dir. Recursively detect systems in this directory

Default: “.”

-r, --rcut

cutoff radius

-t, --type-map

type map

--one-type

treat all types as a single type. Used with se_atten descriptor.

Default: False

examples:: dp neighbor-stat -s data -r 6.0 -t O H

train-nvnmd

train nvnmd model

dp train-nvnmd [-h] [-v {DEBUG,3,INFO,2,WARNING,1,ERROR,0}] [-l LOG_PATH]
               [-s {s1,s2}]
               INPUT

Positional Arguments

INPUT: the input parameter file in json format

Named Arguments

-v, --log-level

Possible choices: DEBUG, 3, INFO, 2, WARNING, 1, ERROR, 0

set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO and 3=DEBUG

Default: “INFO”

-l, --log-path

set log file to log messages to disk, if not specified, the logs will only be output to console

-s, --step

Possible choices: s1, s2

steps to train model of NVNMD: s1 (train CNN), s2 (train QNN)

Default: “s1”

Integrate with third-party packages

Note that the model for inference is required to be compatible with the DeePMD-kit package. See Model compatibility for details.

Use deep potential with ASE

Deep potential can be set up as a calculator with ASE to obtain potential energies and forces.

from ase import Atoms
from deepmd.calculator import DP

water = Atoms('H2O',
              positions=[(0.7601, 1.9270, 1),
                         (1.9575, 1, 1),
                         (1., 1., 1.)],
              cell=[100, 100, 100],
              calculator=DP(model="frozen_model.pb"))
print(water.get_potential_energy())
print(water.get_forces())

Optimization is also available:

from ase.optimize import BFGS
dyn = BFGS(water)
dyn.run(fmax=1e-6)
print(water.get_positions())

Run MD with LAMMPS

Running an MD simulation with LAMMPS is simpler. In the LAMMPS input file, one needs to specify the pair style as follows

pair_style     deepmd graph.pb
pair_coeff     * *

where graph.pb is the file name of the frozen model. It should be noted that LAMMPS counts atom types starting from 1, therefore, all LAMMPS atom type will be firstly subtracted by 1, and then passed into the DeePMD-kit engine to compute the interactions.

LAMMPS commands

Enable DeePMD-kit plugin (plugin mode)

If you are using the plugin mode, enable DeePMD-kit package in LAMMPS with plugin command:

plugin load libdeepmd_lmp.so

After LAMMPS version patch_24Mar2022, another way to load plugins is to set the environmental variable LAMMPS_PLUGIN_PATH:

LAMMPS_PLUGIN_PATH=$deepmd_root/lib/deepmd_lmp

where $deepmd_root is the directory to install C++ interface.

The built-in mode doesn’t need this step.

pair_style `deepmd`

The DeePMD-kit package provides the pair_style deepmd

pair_style deepmd models ... keyword value ...

deepmd = style of this pair_style
models = frozen model(s) to compute the interaction. If multiple models are provided, then only the first model serves to provide energy and force prediction for each timestep of molecular dynamics, and the model deviation will be computed among all models every out_freq timesteps.
keyword = out_file or out_freq or fparam or atomic or relative

    out_file value = filename
        filename = The file name for the model deviation output. Default is model_devi.out
    out_freq value = freq
        freq = Frequency for the model deviation output. Default is 100.
    fparam value = parameters
        parameters = one or more frame parameters required for model evaluation.
    atomic = no value is required. 
        If this keyword is set, the model deviation of each atom will be output.
    relative value = level
        level = The level parameter for computing the relative model deviation

Examples

pair_style deepmd graph.pb
pair_style deepmd graph.pb fparam 1.2
pair_style deepmd graph_0.pb graph_1.pb graph_2.pb out_file md.out out_freq 10 atomic relative 1.0

Description

Evaluate the interaction of the system by using Deep Potential or Deep Potential Smooth Edition. It is noticed that deep potential is not a “pairwise” interaction, but a multi-body interaction.

This pair style takes the deep potential defined in a model file that usually has the .pb extension. The model can be trained and frozen by package DeePMD-kit.

The model deviation evalulate the consistency of the force predictions from multiple models. By default, only the maximal, minimal and averge model deviations are output. If the key atomic is set, then the model deviation of force prediction of each atom will be output.

By default, the model deviation is output in absolute value. If the keyword relative is set, then the relative model deviation will be output. The relative model deviation of the force on atom $i$ is defined by

\[E_{f_i}=\frac{\left|D_{f_i}\right|}{\left|f_i\right|+l}\]

where $D_{f_i}$ is the absolute model deviation of the force on atom $i$, $f_i$ is the norm of the the force and $l$ is provided as the parameter of the keyword relative.

Restrictions

The deepmd pair style is provided in the USER-DEEPMD package, which is compiled from the DeePMD-kit, visit the DeePMD-kit website for more information.

Compute tensorial properties

The DeePMD-kit package provide the compute deeptensor/atom for computing atomic tensorial properties.

compute ID group-ID deeptensor/atom model_file

ID: user-assigned name of the computation
group-ID: ID of the group of atoms to compute
deeptensor/atom: the style of this compute
model_file: the name of the binary model file.

Examples

compute         dipole all deeptensor/atom dipole.pb

The result of the compute can be dump to trajctory file by

dump            1 all custom 100 water.dump id type c_dipole[1] c_dipole[2] c_dipole[3] 

Restrictions

The deeptensor/atom compute is provided in the USER-DEEPMD package, which is compiled from the DeePMD-kit, visit the DeePMD-kit website for more information.

Long-range interaction

The reciprocal space part of the long-range interaction can be calculated by LAMMPS command kspace_style. To use it with DeePMD-kit, one writes

pair_style	deepmd graph.pb
pair_coeff  * *
kspace_style	pppm 1.0e-5
kspace_modify	gewald 0.45

Please notice that the DeePMD does nothing to the direct space part of the electrostatic interaction, because this part is assumed to be fitted in the DeePMD model (the direct space cut-off is thus the cut-off of the DeePMD model). The splitting parameter gewald is modified by the kspace_modify command.

Use of the centroid/stress/atom to get the full 3x3 “atomic-virial”

The DeePMD-kit allows also the computation of per-atom stress tensor defined as:

\[dvatom=\sum_{m}( \mathbf{r}_n- \mathbf{r}_m) \frac{de_m}{d\mathbf{r}_n}\]

Where $\mathbf{r}_n$ is the atomic position of nth atom, $\mathbf{v}_n$ velocity of atom and $\frac{de_m}{d\mathbf{r}_n}$ the derivative of the atomic energy.

In LAMMPS one can get the per-atom stress using the command centroid/stress/atom:

compute ID group-ID centroid/stress/atom NULL virial

see LAMMPS doc page for more detailes on the meaning of the keywords.

Examples

In order of computing the 9-component per-atom stress

compute stress all centroid/stress/atom NULL virial

Thus c_stress is an array with 9 component in the order xx,yy,zz,xy,xz,yz,yx,zx,zy.

If you use this feature please cite D. Tisi, L. Zhang, R. Bertossa, H. Wang, R. Car, S. Baroni - arXiv preprint arXiv:2108.10850, 2021

Computation of heat flux

Using per-atom stress tensor one can, for example, compute the heat flux defined as:

\[\mathbf J = \sum_n e_n \mathbf v_n + \sum_{n,m} ( \mathbf r_m- \mathbf r_n) \frac{de_m}{d\mathbf r_n} \mathbf v_n\]

to compute the heat flux with LAMMPS:

compute ke_ID all ke/atom
compute pe_ID all pe/atom
compute stress_ID group-ID centroid/stress/atom NULL virial
compute flux_ID all heat/flux ke_ID pe_ID stress_ID

Examples

compute ke all ke/atom
compute pe all pe/atom
compute stress all centroid/stress/atom NULL virial
compute flux all heat/flux ke pe stress

c_flux is a global vector of length 6. The first three components are the $x$, $y$ and $z$ components of the full heat flux vector. The others are the components of the so-called convective portion, see LAMMPS doc page for more detailes.

If you use these features please cite D. Tisi, L. Zhang, R. Bertossa, H. Wang, R. Car, S. Baroni - arXiv preprint arXiv:2108.10850, 2021

Run path-integral MD with i-PI

The i-PI works in a client-server model. The i-PI provides the server for integrating the replica positions of atoms, while the DeePMD-kit provides a client named dp_ipi (or dp_ipi_low for low precision) that computes the interactions (including energy, force and virial). The server and client communicates via the Unix domain socket or the Internet socket. Installation instructions of i-PI can be found here. The client can be started by

i-pi input.xml &
dp_ipi water.json

It is noted that multiple instances of the client is allow for computing, in parallel, the interactions of multiple replica of the path-integral MD.

water.json is the parameter file for the client dp_ipi, and an example is provided:

{
    "verbose":		false,
    "use_unix":		true,
    "port":		31415,
    "host":		"localhost",
    "graph_file":	"graph.pb",
    "coord_file":	"conf.xyz",
    "atom_type" : {
	"OW":		0, 
	"HW1":		1,
	"HW2":		1
    }
}

The option use_unix is set to true to activate the Unix domain socket, otherwise, the Internet socket is used.

The option port should be the same as that in input.xml:

<port>31415</port>

The option graph_file provides the file name of the frozen model.

The dp_ipi gets the atom names from an XYZ file provided by coord_file (meanwhile ignores all coordinates in it), and translates the names to atom types by rules provided by atom_type.

Running MD with GROMACS

DP/MM Simulation

This part gives a simple tutorial on how to run a DP/MM simulation for methane in water, which means using DP for methane and TIP3P for water. All relevant files can be found in examples/methane.

Topology Preparation

Similar to QM/MM simulation, the internal interactions (including bond, angle, dihedrals, LJ, Columb) of the region descibed by a neural network potential (NNP) have to be turned off. In GROMACS, bonded interactions can be turned off by modifying [ bonds ], [ angles ], [ dihedrals ] and [ pairs ] sections. And LJ and Columb interactions must be turned off by [ exclusions ] section.

For example, if one wants to simulate ethane in water, using DeepPotential for methane and TIP3P for water, the topology of methane should be like the following (as presented in examples/methane/methane.itp):

[ atomtypes ]
;name btype  mass  charge ptype    sigma  epsilon
  c3    c3   0.0     0.0     A 0.339771 0.451035
  hc    hc   0.0     0.0     A 0.260018 0.087027

[ moleculetype ]
;name            nrexcl
 methane          3

[ atoms ]
; nr type  resnr residue atom  cgnr  charge   mass
  1   c3      1     MOL   C1     1 -0.1068 12.010
  2   hc      1     MOL   H1     2  0.0267  1.008
  3   hc      1     MOL   H2     3  0.0267  1.008
  4   hc      1     MOL   H3     4  0.0267  1.008
  5   hc      1     MOL   H4     5  0.0267  1.008

[ bonds ]
; i  j  func  b0  kb
 1  2     5        
 1  3     5        
 1  4     5        
 1  5     5        

[ exclusions ]
; ai  aj1  aj2  aj3  aj4
  1    2    3    4    5
  2    1    3    4    5
  3    1    2    4    5
  4    1    2    3    5
  5    1    2    3    4

For comparsion, the original topology file genearted by acpype will be:

; methane_GMX.itp created by acpype (v: 2021-02-05T22:15:50CET) on Wed Sep  8 01:21:53 2021

[ atomtypes ]
;name   bond_type     mass     charge   ptype   sigma         epsilon       Amb
 c3       c3          0.00000  0.00000   A     3.39771e-01   4.51035e-01 ; 1.91  0.1078
 hc       hc          0.00000  0.00000   A     2.60018e-01   8.70272e-02 ; 1.46  0.0208

[ moleculetype ]
;name            nrexcl
 methane          3

[ atoms ]
;   nr  type  resi  res  atom  cgnr     charge      mass       ; qtot   bond_type
     1   c3     1   MOL    C1    1    -0.106800     12.01000 ; qtot -0.107
     2   hc     1   MOL    H1    2     0.026700      1.00800 ; qtot -0.080
     3   hc     1   MOL    H2    3     0.026700      1.00800 ; qtot -0.053
     4   hc     1   MOL    H3    4     0.026700      1.00800 ; qtot -0.027
     5   hc     1   MOL    H4    5     0.026700      1.00800 ; qtot 0.000

[ bonds ]
;   ai     aj funct   r             k
     1      2   1    1.0970e-01    3.1455e+05 ;     C1 - H1    
     1      3   1    1.0970e-01    3.1455e+05 ;     C1 - H2    
     1      4   1    1.0970e-01    3.1455e+05 ;     C1 - H3    
     1      5   1    1.0970e-01    3.1455e+05 ;     C1 - H4    

[ angles ]
;   ai     aj     ak    funct   theta         cth
     2      1      3      1    1.0758e+02    3.2635e+02 ;     H1 - C1     - H2    
     2      1      4      1    1.0758e+02    3.2635e+02 ;     H1 - C1     - H3    
     2      1      5      1    1.0758e+02    3.2635e+02 ;     H1 - C1     - H4    
     3      1      4      1    1.0758e+02    3.2635e+02 ;     H2 - C1     - H3    
     3      1      5      1    1.0758e+02    3.2635e+02 ;     H2 - C1     - H4    
     4      1      5      1    1.0758e+02    3.2635e+02 ;     H3 - C1     - H4    

DeepMD Settings

Before running simulation, we need to tell GROMACS to use DeepPotential by setting environment variable GMX_DEEPMD_INPUT_JSON:

export GMX_DEEPMD_INPUT_JSON=input.json

Then, in your working directories, we have to write input.json file:

{
    "graph_file": "/path/to/graph.pb",
    "type_file": "type.raw",
    "index_file": "index.raw",
    "lambda": 1.0,
    "pbc": false
}

Here is an explanation for these settings:

graph_file : The graph file (with suffix .pb) generated by dp freeze command
type_file : File to specify DP atom types (in space-sepreated format). Here, type.raw looks like

1 0 0 0 0

index_file : File containing indices of DP atoms (in space-seperated format), which should be in consistent with indices’ order in .gro file but starting from zero. Here, index.raw looks like

0 1 2 3 4

lambda: Optional, default 1.0. Used in alchemical calculations.
pbc: Optional, default true. If true, the GROMACS peroidic condition is passed to DeepMD.

Run Simulation

Finally, you can run GROMACS using gmx mdrun as usual.

All-atom DP Simulation

This part gives an example on how to run a simulation with all atoms described by a DeepPotential with Gromacs, taking water as an example. Instead of using [ exclusions ] to turn off the non-bonded energies, we can simply do this by setting LJ parameters (i.e. epsilon and sigma) and partial charges to 0, as shown in examples/water/gmx/water.top:

[ atomtypes ]
; name      at.num  mass     charge ptype  sigma      epsilon
HW           1       1.008   0.0000  A   0.00000e+00  0.00000e+00
OW           8      16.00    0.0000  A   0.00000e+00  0.00000e+00

As mentioned in the above section, input.json and relevant files (index.raw, type.raw) should also be created. Then, we can start the simulation under NVT ensemble and plot the radial distribution function (RDF) by gmx rdf command. We can see that the RDF given by Gromacs+DP matches perfectly with Lammps+DP, which further provides an evidence on the validity of our simulation. rdf

However, we still recommend you run all-atom DP simulation using LAMMPS since it is more stable and efficient.

Interfaces out of DeePMD-kit

The codes of the following interfaces are not a part of the DeePMD-kit package and maintained by other repositories. We list these interfaces here for user convenience.

dpdata

dpdata provides the predict method for System class:

import dpdata
dsys = dpdata.LabeledSystem('OUTCAR')
dp_sys = dsys.predict("frozen_model_compressed.pb")

By inferring with the DP model frozen_model_compressed.pb, dpdata will generate a new labeled system dp_sys with inferred energies, forces, and virials.

OpenMM plugin for DeePMD-kit

An OpenMM plugin is provided from JingHuangLab/openmm_deepmd_plugin, written by the Huang Lab at the Westlake University.

AMBER interface to DeePMD-kit

An AMBER interface to DeePMD-kit is written by the York Lab from the Rutgers University. It is open-source at GitLab RutgersLBSR/AmberDPRc. Details can be found in this paper.

DP-GEN

DP-GEN provides a workflow to generate accurate DP models by calling DeePMD-kit’s command line interface (CLI) in the local or the remote server. Details can be found in this paper.

MLatom

Mlatom provides an interface to the DeePMD-kit within MLatom’s workflow by calling DeePMD-kit’s CLI. Details can be found in this paper.

Use NVNMD

Introduction

NVNMD stands for non-von Neumann molecular dynamics.

This is the training code we used to generate the results in our paper entitled “Accurate and Efficient Molecular Dynamics based on Machine Learning and Non Von Neumann Architecture”, which has been accepted by npj Computational Materials (DOI: 10.1038/s41524-022-00773-z).

Any user can follow two consecutive steps to run molecular dynamics (MD) on the proposed NVNMD computer, which has been released online: (i) to train a machine learning (ML) model that can decently reproduce the potential energy surface (PES); and (ii) to deploy the trained ML model on the proposed NVNMD computer, then run MD there to obtain the atomistic trajectories.

Training

Our training procedure consists of not only the continuous neural network (CNN) training, but also the quantized neural network (QNN) training which uses the results of CNN as inputs. It is performed on CPU or GPU by using the training codes we open-sourced online.

To train a ML model that can decently reproduce the PES, training and testing data set should be prepared first. This can be done by using either the state-of-the-art active learning tools, or the outdated (i.e., less efficient) brute-force density functional theory (DFT)-based ab-initio molecular dynamics (AIMD) sampling.

If you just want to simply test the training function, you can use the example in the $deepmd_source_dir/examples/nvnmd directory. If you want to fully experience training and running MD functions, you can download the complete example from the website.

Then, copy the data set to working directory

mkdir -p $workspace
cd $workspace
mkdir -p data
cp -r $dataset data

where $dataset is the path to the data set and $workspace is the path to working directory.

Input script

Create and go to the training directory.

mkdir train
cd train 

Then copy the input script train_cnn.json and train_qnn.json to the directory train

cp -r $deepmd_source_dir/examples/nvnmd/train/train_cnn.json train_cnn.json
cp -r $deepmd_source_dir/examples/nvnmd/train/train_qnn.json train_qnn.json

The structure of the input script is as follows

{
    "nvnmd" : {},
    "learning_rate" : {},
    "loss" : {},
    "training": {}
}

nvnmd

The “nvnmd” section is defined as

{
    "net_size":128,
    "sel":[60, 60],
    "rcut":6.0,
    "rcut_smth":0.5
}

where items are defined as:

Item	Mean	Optional Value
net_size	the size of nueral network	128
sel	the number of neighbors	integer list of lengths 1 to 4 are acceptable
rcut	the cutoff radial	(0, 8.0]
rcut_smth	the smooth cutoff parameter	(0, 8.0]

learning_rate

The “learning_rate” section is defined as

{
    "type":"exp",
    "start_lr": 1e-3,
    "stop_lr": 3e-8,
    "decay_steps": 5000
}

where items are defined as:

Item	Mean	Optional Value
type	learning rate variant type	exp
start_lr	the learning rate at the beginning of the training	a positive real number
stop_lr	the desired learning rate at the end of the training	a positive real number
decay_stops	the learning rate is decaying every {decay_stops} training steps	a positive integer

loss

The “loss” section is defined as

{
    "start_pref_e": 0.02,
    "limit_pref_e": 2,
    "start_pref_f": 1000,
    "limit_pref_f": 1,
    "start_pref_v": 0,
    "limit_pref_v": 0
}

where items are defined as:

Item	Mean	Optional Value
start_pref_e	the loss factor of energy at the beginning of the training	zero or positive real number
limit_pref_e	the loss factor of energy at the end of the training	zero or positive real number
start_pref_f	the loss factor of force at the beginning of the training	zero or positive real number
limit_pref_f	the loss factor of force at the end of the training	zero or positive real number
start_pref_v	the loss factor of virial at the beginning of the training	zero or positive real number
limit_pref_v	the loss factor of virial at the end of the training	zero or positive real number

training

The “training” section is defined as

{
  "seed": 1,
    "stop_batch": 1000000,
    "numb_test": 1,
    "disp_file": "lcurve.out",
    "disp_freq": 1000,
    "save_ckpt": "model.ckpt",
    "save_freq": 10000,
    "training_data":{
      "systems":["system1_path", "system2_path", "..."],
      "set_prefix": "set",
      "batch_size": ["batch_size_of_system1", "batch_size_of_system2", "..."]
    }
}

where items are defined as:

Item	Mean	Optional Value
seed	the randome seed	a integer
stop_batch	the total training steps	a positive integer
numb_test	the accuracy is test by using {numb_test} sample	a positive integer
disp_file	the log file where the training message display	a string
disp_freq	display frequency	a positive integer
save_ckpt	check point file	a string
save_freq	save frequency	a positive integer
systems	a list of data directory which contains the dataset	string list
set_prefix	the prefix of dataset	a string
batch_size	a list of batch size of corresponding dataset	a integer list

Training

Training can be invoked by

# step1: train CNN
dp train-nvnmd train_cnn.json -s s1
# step2: train QNN
dp train-nvnmd train_qnn.json -s s2

After training process, you will get two folders: nvnmd_cnn and nvnmd_qnn. The nvnmd_cnn contains the model after continuous neural network (CNN) training. The nvnmd_qnn contains the model after quantized neural network (QNN) training. The binary file nvnmd_qnn/model.pb is the model file which is used to performs NVNMD in server [http://nvnmd.picp.vip]

Testing

The frozen model can be used in many ways. The most straightforward testing can be invoked by

mkdir test
dp test -m ./nvnmd_qnn/frozen_model.pb -s path/to/system -d ./test/detail -n 99999 -l test/output.log

where the frozen model file to import is given via the -m command line flag, the path to the testing data set is given via the -s command line flag, the file containing details of energy, force and virial accuracy is given via the -d command line flag, the amount of data for testing is given via the -n command line flag.

Running MD

After CNN and QNN training, you can upload the ML model to our online NVNMD system and run MD there.

Account application

The server website of NVNMD is available at http://nvnmd.picp.vip. You can visit the URL and enter the login interface (Figure.1).

ALT

Figure.1 The login interface

To obtain an account, please send your application to the email (jie_liu@hnu.edu.cn, liujie@uw.edu). The username and password will be sent to you by email.

Adding task

After successfully obtaining the account, enter the username and password in the login interface, and click “Login” to enter the homepage (Figure.2).

ALT

Figure.2 The homepage

The homepage displays the remaining calculation time and all calculation records not deleted. Click Add a new task to enter the interface for adding a new task (Figure.3).

ALT

Figure.3 The interface for adding a new task

Task name: name of the task
Upload mode: two modes of uploading results to online data storage, including Manual upload and Automatic upload. Results need to be uploaded manually to online data storage with Manual upload mode, and will be uploaded automatically with Automatic upload mode.
Input script: input file of the MD simulation.

In the input script, one needs to specify the pair style as follows

pair_style nvnmd model.pb
pair_coeff * *

Model file: the ML model named model.pb obtained by QNN training.
Data files: data files containing information required for running an MD simulation (e.g., coord.lmp containing initial atom coordinates).

Next, you can click Submit to submit the task and then automatically return to the homepage (Figure.4).

ALT

Figure.4 The homepage with a new record

Then, click Refresh to view the latest status of all calculation tasks.

Cancelling calculation

For the task whose calculation status is Pending and Running, you can click the corresponding Cancel on the homepage to stop the calculation (Figure.5).

ALT

Figure.5 The homepage with a cancelled task

Downloading results

For the task whose calculation status is Completed, Failed and Cancelled, you can click the corresponding Package or Separate files in the Download results bar on the homepage to download results.

Click Package to download a zipped package of all files including input files and output results (Figure.6).

ALT

Figure.6 The interface for downloading a zipped package

Click Separate files to download the required separate files (Figure.7).

ALT

Figure.7 The interface for downloading separate files

If Manual upload mode is selected or the file has expired, click Upload on the download interface to upload manually.

Deleting record

For the task no longer needed, you can click the corresponding Delete on the homepage to delete the record.

Records cannot be retrieved after deletion.

Clearing records

Click Clear calculation records on the homepage to clear all records.

Records cannot be retrieved after clearing.

FAQs

In consequence of various differences of computers or systems, problems may occur. Some common circumstances are listed as follows. In addition, some frequently asked questions about parameters setting are listed as follows. If other unexpected problems occur, you’re welcome to contact us for help.

How to tune Fitting/embedding-net size ?

Here are some test forms on fitting-net size tuning or embedding-net size tuning performed on several different systems.

Al2O3

Fitting net size tuning form on Al2O3: (embedding-net size: [25,50,100])

Fitting-net size	Energy L2err(eV)	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[240,240,240]	1.742252e-02	7.259383e-05	4.014115e-02
[80,80,80]	1.799349e-02	7.497287e-05	4.042977e-02
[40,40,40]	1.799036e-02	7.495984e-05	4.068806e-02
[20,20,20]	1.834032e-02	7.641801e-05	4.094784e-02
[10,10,10]	1.913058e-02	7.971073e-05	4.154775e-02
[5,5,5]	1.932914e-02	8.053808e-05	4.188052e-02
[4,4,4]	1.944832e-02	8.103467e-05	4.217826e-02
[3,3,3]	2.068631e-02	8.619296e-05	4.300497e-02
[2,2,2]	2.267962e-02	9.449840e-05	4.413609e-02
[1,1,1]	2.813596e-02	1.172332e-04	4.781115e-02
[]	3.135002e-02	1.306251e-04	5.373120e-02

[] means no hidden layer, but there is still a linear output layer. This situation is equal to the linear regression.

Embedding net size tuning form on Al2O3: (Fitting-net size: [240,240,240])

Embedding-net size	Energy L2err(eV)	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[25,50,100]	1.742252e-02	7.259383e-05	4.014115e-02
[10,20,40]	2.909990e-02	1.212496e-04	4.734667e-02
[5,10,20]	3.357767e-02	1.399070e-04	5.706385e-02
[4,8,16]	6.060367e-02	2.525153e-04	7.333304e-02
[3,6,12]	5.656043e-02	2.356685e-04	7.793539e-02
[2,4,8]	5.277023e-02	2.198759e-04	7.459995e-02
[1,2,4]	1.302282e-01	5.426174e-04	9.672238e-02

Cu

Fitting net size tuning form on Cu: (embedding-net size: [25,50,100])

Fitting-net size	Energy L2err(eV)	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[240,240,240]	4.135548e-02	1.615449e-04	8.940946e-02
[20,20,20]	4.323858e-02	1.689007e-04	8.955762e-02
[10,10,10]	4.399364e-02	1.718502e-04	8.962891e-02
[5,5,5]	4.468404e-02	1.745470e-04	8.970111e-02
[4,4,4]	4.463580e-02	1.743586e-04	8.972011e-02
[3,3,3]	4.493758e-02	1.755374e-04	8.971303e-02
[2,2,2]	4.500736e-02	1.758100e-04	8.973878e-02
[1,1,1]	4.542073e-02	1.774247e-04	8.964761e-02
[]	4.545168e-02	1.775456e-04	8.983201e-02

Embedding net size tuning form on Cu: (Fitting-net size: [240,240,240])

Embedding-net size	Energy L2err(eV)	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[25,50,100]	4.135548e-02	1.615449e-04	8.940946e-02
[20,40,80]	4.203562e-02	1.642016e-04	8.925881e-02
[15,30,60]	4.146672e-02	1.619794e-04	8.936911e-02
[10,20,40]	4.263060e-02	1.665258e-04	8.955818e-02
[5,10,20]	4.994913e-02	1.951138e-04	9.007786e-02
[4,8,16]	1.022157e-01	3.992802e-04	9.532119e-02
[3,6,12]	1.362098e-01	5.320695e-04	1.073860e-01
[2,4,8]	7.061800e-02	2.758515e-04	9.126418e-02
[1,2,4] && seed = 1	9.843161e-02	3.844985e-04	9.348505e-02
[1,2,4] && seed = 2	9.404335e-02	3.673568e-04	9.304089e-02
[1,2,4] && seed = 3	1.508016e-01	5.890688e-04	1.382356e-01
[1,2,4] && seed = 4	9.686949e-02	3.783965e-04	9.294820e-02

Water

Fitting net size tuning form on water: (embedding-net size: [25,50,100])

Fitting-net size	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[240,240,240]	9.1589E-04	5.1540E-02
[200,200,200]	9.3221E-04	5.2366E-02
[160,160,160]	9.4274E-04	5.3403E-02
[120,120,120]	9.5407E-04	5.3093E-02
[80,80,80]	9.4605E-04	5.3402E-02
[40,40,40]	9.8533E-04	5.5790E-02
[20,20,20]	1.0057E-03	5.8232E-02
[10,10,10]	1.0466E-03	6.2279E-02
[5,5,5]	1.1154E-03	6.7994E-02
[4,4,4]	1.1289E-03	6.9613E-02
[3,3,3]	1.2368E-03	7.9786E-02
[2,2,2]	1.3558E-03	9.7042E-02
[1,1,1]	1.4633E-03	1.1265E-01
[]	1.5193E-03	1.2136E-01

Embedding net size tuning form on water: (Fitting-net size: [240,240,240])

Embedding-net size	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[25,50,100]	9.1589E-04	5.1540E-02
[20,40,80]	9.5080E-04	5.3593E-02
[15,30,60]	9.7996E-04	5.6338E-02
[10,20,40]	1.0353E-03	6.2776E-02
[5,10,20]	1.1254E-03	7.3195E-02
[4,8,16]	1.2495E-03	8.0371E-02
[3,6,12]	1.3604E-03	9.9883E-02
[2,4,8]	1.4358E-03	9.7389E-02
[1,2,4]	2.1765E-03	1.7276E-01

Mg-Al

Fitting net size tuning form on Mg-Al: (embedding-net size: [25,50,100])

Fitting-net size	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[240,240,240]	3.9606e-03	1.6289e-02
[200,200,200]	3.9449e-03	1.6471e-02
[160,160,160]	4.0947e-03	1.6413e-02
[120,120,120]	3.9234e-03	1.6283e-02
[80,80,80]	3.9758e-03	1.6506e-02
[40,40,40]	3.9142e-03	1.6348e-02
[20,20,20]	4.1302e-03	1.7006e-02
[10,10,10]	4.3433e-03	1.7524e-02
[5,5,5]	5.3154e-03	1.9716e-02
[4,4,4]	5.4210e-03	1.9710e-02
[2,2,2]	6.2667e-03	2.2568e-02
[1,1,1]	7.3676e-03	2.6375e-02
[]	7.3999e-03	2.6097e-02

Embedding net size tuning form on Mg-Al: (Fitting-net size: [240,240,240])

Embedding-net size	Energy L2err/Natoms(eV)	Force L2err(eV/Angstrom)
[25,50,100]	3.9606e-03	1.6289e-02
[20,40,80]	4.0292e-03	1.6555e-02
[15,30,60]	4.1743e-03	1.7026e-02
[10,20,40]	4.8138e-03	1.8516e-02
[5,10,20]	5.6052e-03	2.0709e-02
[4,8,16]	6.1335e-03	2.1450e-02
[3,6,12]	6.6469e-03	2.3003e-02
[2,4,8]	6.8222e-03	2.6318e-02
[1,2,4]	1.0678e-02	3.9559e-02

How to control the number of nodes used by a job ?

Set the number of CPU nodes used by DP algorithms with:

mpirun -np $num_nodes dp

Set the number of threads used by DP algorithms with:

export OMP_NUM_THREADS=$num_threads

Set the number of CPU nodes used by TF kernels with:

export TF_INTRA_OP_PARALLELISM_THREADS=$num_nodes
export TF_INTER_OP_PARALLELISM_THREADS=$num_nodes

Do we need to set rcut < half boxsize ?

When seeking the neighbors of atom i under periodic boundary condition, deepmd-kit considers all j atoms within cutoff rcut from atom i in all mirror cells.

So, so there is no limitation on the setting of rcut.

PS: The reason why some softwares require rcut < half boxsize is that they only consider the nearest mirrors from the center cell. Deepmd-kit is totally different from them.

How to set sel ?

sel is short for “selected number of atoms in rcut”.

sel_a[i] is a list of integers. The length of the list should be the same as the number of atom types in the system.

sel_a[i] gives the number of selected number of type i neighbors within rcut. To ensure that the results are strictly accurate, sel_a[i] should be larger than the largest number of type i neighbors in the rcut.

However, the computation overhead increases with sel_a[i], therefore, sel_a[i] should be as small as possible.

The setting of sel_a[i] should balance the above two considerations.

Installation

Inadequate versions of gcc/g++

Sometimes you may use a gcc/g++ of version < 4.8. In this way, you can still compile all the parts of TensorFlow and most of the parts of DeePMD-kit, but i-Pi and GROMACS plugin will be disabled automatically. Or if you have a gcc/g++ of version > 4.8, say, 7.2.0, you may choose to use it by doing

export CC=/path/to/gcc-7.2.0/bin/gcc
export CXX=/path/to/gcc-7.2.0/bin/g++

Build files left in DeePMD-kit

When you try to build a second time when installing DeePMD-kit, files produced before may contribute to failure. Thus, you may clear them by

cd build
rm -r *

and redo the cmake process.

The temperature undulates violently during early stages of MD

This is probably because your structure is too far from the equlibrium configuration.

Although, to make sure the potential model is truly accurate, we recommend to check model deviation.

MD: cannot run LAMMPS after installing a new version of DeePMD-kit

This typically happens when you install a new version of DeePMD-kit and copy directly the generated USER-DEEPMD to a LAMMPS source code folder and re-install LAMMPS.

To solve this problem, it suffices to first remove USER-DEEPMD from LAMMPS source code by

make no-user-deepmd

and then install the new USER-DEEPMD.

If this does not solve your problem, try to decompress the LAMMPS source tarball and install LAMMPS from scratch again, which typically should be very fast.

Model compatibility

When the version of DeePMD-kit used to training model is different from the that of DeePMD-kit running MDs, one has the problem of model compatibility.

DeePMD-kit guarantees that the codes with the same major and minor revisions are compatible. That is to say v0.12.5 is compatible to v0.12.0, but is not compatible to v0.11.0 nor v1.0.0.

One can execute dp convert-from to convert an old model to a new one.

Model version	v0.12	v1.0	v1.1	v1.2	v1.3	v2.0	v2.1
Compatibility	😊	😊	😊	😊	😊	😄	😄

Legend:

😄: The model is compatible with the DeePMD-kit package.
😊: The model is incompatible with the DeePMD-kit package, but one can execute dp convert-from to convert an old model to v2.1.
😢: The model is incompatible with the DeePMD-kit package, and there is no way to convert models.

Coding Conventions

Preface

The aim of these coding standards is to help create a codebase with defined and consistent coding style that every contributor can get easily familiar with. This will in enhance code readability as there will be no different coding styles from different contributors and everything will be documented. Also PR diffs will be smaller because of unified coding style. Finally static typing will help in hunting down potential bugs before the code is even run.

Contributed code will not be refused merely because it does not strictly adhere to these conditions; as long as it’s internally consistent, clean, and correct, it probably will be accepted. But don’t be surprised if the “offending” code gets fiddled over time to conform to these conventions.

There are also github actions CI checks for python code style which will annotate the PR diff for you to see the areas where your code is lacking compared to the set standard.

Rules

The code must be compatible with the oldest supported version of python which is 3.6

The project follows the generic coding conventions as specified in the Style Guide for Python Code, Docstring Conventions and Typing Conventions PEPs, clarified and extended as follows:

Do not use “*” imports such as from module import *. Instead, list imports explicitly.
Use 4 spaces per indentation level. No tabs.
No one-liner compound statements (i.e., no if x: return: use two lines).
Maximum line length is 88 characters as recomended by black wich is less strict than Docstring Conventions suggests.
Use “StudlyCaps” for class names.
Use “lowercase” or “lowercase_with_underscores” for function, method, variable names and module names. For short names, joined lowercase may be used (e.g. “tagname”). Choose what is most readable.
No single-character variable names, except indices in loops that encompass a very small number of lines (for i in range(5): ...).
Avoid lambda expressions. Use named functions instead.
Avoid functional constructs (filter, map, etc.). Use list comprehensions instead.
Use "double quotes" for string literals, and """triple double quotes""" for docstring’s. Single quotes are OK for something like
```
f"something {'this' if x else 'that'}"
```
Use f-strings s = f"{x:.2f}" instead of old style formating with "%f" % x. string format method "{x:.2f}".format() may be used sparsely where it is more convenient than f-strings.

Whitespace

Python is not C/C++ so whitespace should be used sparingly to maintain code readability

Read the Whitespace in Expressions and Statements section of PEP8.
Avoid trailing whitespaces.
Do not use excessive whitespace in your expressions and statements.
You should have blank spaces after commas, colons, and semi-colons if it isn’t trailing next to the end of a bracket, brace, or parentheses.
With any operators you should use a space in on both sides of the operator.
Colons for slicing are considered a binary operator, and should not have any spaces between them.
You should have parentheses with no space, directly next to the function when calling functions function().
When indexing or slicing the brackets should be directly next to the collection with no space collection["index"].
Whitespace used to line up variable values is not recommended.
Make sure you are consistent with the formats you choose when optional choices are available.

General advice

Get rid of as many break and continue statements as possible.
Write short functions. All functions should fit within a standard screen.
Use descriptive variable names.

Writing documentation in the code

Here is an example of how to write good docstrings:

https://github.com/numpy/numpy/blob/master/doc/example.py

The numpy doctring documentation can be found here

It is a good practice to run pydocstyle check on your code or use a text editor that does it automatically):

$ pydocstyle filename.py

Run pycodestyle on your code

It’s a good idea to run pycodestyle on your code (or use a text editor that does it automatically):

$ pycodestyle filename.py

Run mypy on your code

It’s a good idea to run mypy on your code (or use a text editor that does it automatically):

$ mypy filename.py

Run pydocstyle on your code

It’s a good idea to run pycodestyle on your code (or use a text editor that does it automatically):

$ pycodestyle filename.py --max-line-length=88

Run black on your code

Another method of enforcing PEP8 is using a tool such as black. These tools tend to be very effective at cleaning up code, but should be used carefully and code should be retested after cleaning it. Try:

$ black --help

Create a model

If you’d like to create a new model that isn’t covered by the existing DeePMD-kit library, but reuse DeePMD-kit’s other efficient module such as data processing, trainner, etc, you may want to read this section.

To incorporate your custom model you’ll need to:

Register and implement new components (e.g. descriptor) in a Python file. You may also want to regiester new TensorFlow OPs if necessary.
Register new arguments for user inputs.
Package new codes into a Python package.
Test new models.

Design a new component

When creating a new component, take descriptor as the example, you should inherit deepmd.descriptor.descriptor.Descriptor class and override several methods. Abstract methods such as deepmd.descriptor.descriptor.Descriptor.build must be implemented and others are not. You should keep arguments of these methods unchanged.

After implementation, you need to register the component with a key:

from deepmd.descriptor import Descriptor

@Descriptor.register("some_descrpt")
class SomeDescript(Descriptor):
    def __init__(self, arg1: bool, arg2: float) -> None:
        pass

Register new arguments

To let some one uses your new component in their input file, you need to create a new methods that returns some Argument of your new component, and then register new arguments. For example, the code below

from typing import List

from dargs import Argument
from deepmd.utils.argcheck import descrpt_args_plugin

@descrpt_args_plugin.register("some_descrpt")
def descrpt_some_args() -> List[Argument]:
    return [
        Argument("arg1", bool, optional=False, doc="balabala"),
        Argument("arg2", float, optional=True, default=6.0, doc="haha"),
    ]

allows one to use your new descriptor as below:

"descriptor" :{
    "type": "some_descrpt",
    "arg1": true,
    "arg2": 6.0
}

The arguments here should be consistent with the class arguments of your new componenet.

Package new codes

You may use setuptools to package new codes into a new Python package. It’s crirical to add your new component to entry_points['deepmd'] in setup.py:

    entry_points={
        'deepmd': [
            'some_descrpt=deepmd_some_descrtpt:SomeDescript',
        ],
    },

where deepmd_some_descrtpt is the module of your codes. It is equivalent to from deepmd_some_descrtpt import SomeDescript.

If you place SomeDescript and descrpt_some_args into different modules, you are also expected to add descrpt_some_args to entry_points.

After you install your new package, you can now use dp train to run your new model.

Atom Type Embedding

Overview

Here is an overview of the deepmd-kit algorithm. Given a specific centric atom, we can obtain the matrix describing its local environment, named as $\mathcal R$. It is consist of the distance between centric atom and its neighbors, as well as a direction vector. We can embed each distance into a vector of $M_1$ dimension by an embedding net, so the environment matrix $\mathcal R$ can be embed into matrix $\mathcal G$. We can thus extract a descriptor vector (of $M_1 \times M_2$ dim) of the centric atom from the $\mathcal G$ by some matrix multiplication, and put the descriptor into fitting net to get predicted energy $E$. The vanilla version of deepmd-kit build embedding net and fitting net relying on the atom type, resulting in $O(N)$ memory usage. After applying atom type embedding, in deepmd-kit v2.0, we can share one embedding net and one fitting net in total, which decline training complexity largely.

Preliminary

In the following chart, you can find the meaning of symbols used to clarify the atom type embedding algorithm.

$i$: Type of centric atom

$j$: Type of neighbor atom

$s_{ij}$: Distance between centric atom and neighbor atom

$\mathcal G_{ij}(\cdot)$: Origin embedding net, take $s_{ij}$ as input and output embedding vector of $M_1$ dim

$\mathcal G(\cdot)$: Shared embedding net

$\text{Multi}(\cdot)$: Matrix multiplication and flattening, output the descriptor vector of $M_1\times M_2$ dim

$F_i(\cdot)$: Origin fitting net, take the descriptor vector as input and output energy

$F(\cdot)$: Shared fitting net

$A(\cdot)$: Atom type embedding net, input is atom type, output is type embedding vector of dim nchanl

So, we can formulate the training process as follows. Vanilla deepmd-kit algorithm:

\[E = F_i( \text{Multi}( \mathcal G_{ij}( s_{ij} ) ) )\]

Deepmd-kit applying atom type embedding:

\[E = F( [ \text{Multi}( \mathcal G( [s_{ij}, A(i), A(j)] ) ), A(j)] )\]

or

\[E = F( [ \text{Multi}( \mathcal G( [s_{ij}, A(j)] ) ), A(j)] )\]

The difference between two variants above is whether using the information of centric atom when generating the descriptor. Users can choose by modifying the type_one_side hyper-parameter in the input json file.

How to use

A detailed introduction can be found at se_e2_a_tebd. Looking for a fast start up, you can simply add a type_embedding section in the input json file as displayed in the following, and the algorithm will adopt atom type embedding algorithm automatically. An example of type_embedding is like

    "type_embedding":{
       "neuron":    [2, 4, 8],
       "resnet_dt": false,
       "seed":      1
    }

Code Modification

Atom type embedding can be applied to varied embedding net and fitting net, as a result we build a class TypeEmbedNet to support this free combination. In the following, we will go through the execution process of the code to explain our code modification.

trainer (train/trainer.py)

In trainer.py, it will parse the parameter from the input json file. If a type_embedding section is detected, it will build a TypeEmbedNet, which will be later input in the model. model will be built in the function _build_network.

model (model/ener.py)

When building the operation graph of the model in model.build. If a TypeEmbedNet is detected, it will build the operation graph of type embed net, embedding net and fitting net by order. The building process of type embed net can be found in TypeEmbedNet.build, which output the type embedding vector of each atom type (of [$\text{ntypes} \times \text{nchanl}$] dimensions). We then save the type embedding vector into input_dict, so that they can be fetched later in embedding net and fitting net.

embedding net (descriptor/se*.py)

In embedding net, we shall take local environment $\mathcal R$ as input and output matrix $\mathcal G$. Functions called in this process by order is

build -> _pass_filter -> _filter -> _filter_lower 

_pass_filter: It will first detect whether an atom type embedding exists, if so, it will apply atom type embedding algorithm and doesn’t divide the input by type.

_filter: It will call _filter_lower function to obtain the result of matrix multiplication ($\mathcal G^T\cdot \mathcal R$), do further multiplication involved in $\text{Multi}(\cdot)$, and finally output the result of descriptor vector of $M_1 \times M_2$ dim.

_filter_lower: The main function handling input modification. If type embedding exists, it will call _concat_type_embedding function to concat the first column of input $\mathcal R$ (the column of $s_{ij}$) with the atom type embedding information. It will decide whether using the atom type embedding vector of centric atom according to the value of type_one_side (if set True, then we only use the vector of the neighbor atom). The modified input will be put into the fitting net to get $\mathcal G$ for further matrix multiplication stage.

fitting net (fit/ener.py)

In fitting net, it take the descriptor vector as input, whose dimension is [natoms, $M_1\times M_2$]. Because we need to involve information of centric atom in this step, we need to generate a matrix named as atype_embed (of dim [natoms, nchanl]), in which each row is the type embedding vector of the specific centric atom. The input is sorted by type of centric atom, we also know the number of a particular atom type (stored in natoms[2+i]), thus we get the type vector of centric atom. In the build phrase of fitting net, it will check whether type embedding exist in input_dict and fetch them. After that calling embed_atom_type function to lookup embedding vector for type vector of centric atom to obtain atype_embed, and concat input with it ([input, atype_embed]). The modified input go through fitting net to get predicted energy.

Note

You can’t apply compression method while using atom type embedding.

Python API

deepmd package

Root of the deepmd package, exposes all public classes and submodules.

class deepmd.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = False)[source]

Bases: object

Common methods for DeepPot, DeepWFC, DeepPolar, …

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: False: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

load_prefix: str

make_natoms_vec(atom_types: numpy.ndarray) → numpy.ndarray[source]

Make the natom vector used by deepmd-kit.

Parameters

atom_types: The type of atoms

Returns

natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

property model_type: str

Get type of model.

:type:str

property model_version: str

Get version of model.

Returns

str: version of model

static reverse_map(vec: numpy.ndarray, imap: List[int]) → numpy.ndarray[source]

Reverse mapping of a vector according to the index map

Parameters

vec: Input vector. Be of shape [nframes, natoms, -1]
imap: Index map. Be of shape [natoms]

Returns

vec_out: Reverse mapped vector.

property sess: tensorflow.python.client.session.Session: Get TF session.

static sort_input(coord: numpy.ndarray, atom_type: numpy.ndarray, sel_atoms: Optional[List[int]] = None)[source]

Sort atoms in the system according their types.

Parameters

coord: The coordinates of atoms. Should be of shape [nframes, natoms, 3]
atom_type: The type of atoms Should be of shape [natoms]
sel_atom: The selected atoms by type

Returns

coord_out: The coordinates after sorting
atom_type_out: The atom types after sorting
idx_map: The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]
sel_atom_type: Only output if sel_atoms is not None The sorted selected atom types
sel_idx_map: Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.

deepmd.DeepPotential(model_file: Union[str, pathlib.Path], load_prefix: str = 'load', default_tf_graph: bool = False) → Union[deepmd.infer.deep_dipole.DeepDipole, deepmd.infer.deep_polar.DeepGlobalPolar, deepmd.infer.deep_polar.DeepPolar, deepmd.infer.deep_pot.DeepPot, deepmd.infer.deep_wfc.DeepWFC][source]

Factory function that will inialize appropriate potential read from model_file.

Parameters

model_file: str: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Returns

Union[DeepDipole, DeepGlobalPolar, DeepPolar, DeepPot, DeepWFC]: one of the available potentials

Raises

RuntimeError: if model file does not correspond to any implementd potential

class deepmd.DipoleChargeModifier(model_name: str, model_charge_map: List[float], sys_charge_map: List[float], ewald_h: float = 1, ewald_beta: float = 1)[source]

Bases: deepmd.infer.deep_dipole.DeepDipole

Parameters

model_name: The model file for the DeepDipole model
model_charge_map: Gives the amount of charge for the wfcc
sys_charge_map: Gives the amount of charge for the real atoms
ewald_h: Grid spacing of the reciprocal part of Ewald sum. Unit: A
ewald_beta: Splitting parameter of the Ewald sum. Unit: A^{-1}

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`build_fv_graph`()	Build the computational graph for the force and virial inference.
`eval`(coord, box, atype[, eval_fv])	Evaluate the modification
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`modify_data`(data)	Modify data.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

build_fv_graph() → tensorflow.python.framework.ops.Tensor[source]: Build the computational graph for the force and virial inference.

eval(coord: numpy.ndarray, box: numpy.ndarray, atype: numpy.ndarray, eval_fv: bool = True) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Evaluate the modification

Parameters

coord: The coordinates of atoms
box: The simulation region. PBC is assumed
atype: The atom types
eval_fv: Evaluate force and virial

Returns

tot_e: The energy modification
tot_f: The force modification
tot_v: The virial modification

load_prefix: str

modify_data(data: dict) → None[source]

Modify data.

Parameters

data: Internal data of DeepmdData. Be a dict, has the following keys - coord coordinates - box simulation box - type atom types - find_energy tells if data has energy - find_force tells if data has force - find_virial tells if data has virial - energy energy - force force - virial virial

Subpackages

deepmd.cluster package

Module that reads node resources, auto detects if running local or on SLURM.

deepmd.cluster.get_resource() → Tuple[str, List[str], Optional[List[int]]][source]

Get local or slurm resources: nodename, nodelist, and gpus.

Returns

Tuple[str, List[str], Optional[List[int]]]: nodename, nodelist, and gpus

Submodules

deepmd.cluster.local module

Get local GPU resources.

deepmd.cluster.local.get_gpus()[source]

Get available IDs of GPU cards at local. These IDs are valid when used as the TensorFlow device ID.

Returns

Optional[List[int]]: List of available GPU IDs. Otherwise, None.

deepmd.cluster.local.get_resource() → Tuple[str, List[str], Optional[List[int]]][source]

Get local resources: nodename, nodelist, and gpus.

Returns

Tuple[str, List[str], Optional[List[int]]]: nodename, nodelist, and gpus

deepmd.cluster.slurm module

MOdule to get resources on SLURM cluster.

References

https://github.com/deepsense-ai/tensorflow_on_slurm ####

deepmd.cluster.slurm.get_resource() → Tuple[str, List[str], Optional[List[int]]][source]

Get SLURM resources: nodename, nodelist, and gpus.

Returns

Tuple[str, List[str], Optional[List[int]]]: nodename, nodelist, and gpus

Raises

RuntimeError: if number of nodes could not be retrieved
ValueError: list of nodes is not of the same length sa number of nodes
ValueError: if current nodename is not found in node list

deepmd.descriptor package

Submodules

deepmd.descriptor.descriptor module

class deepmd.descriptor.descriptor.Descriptor(*args, **kwargs)[source]

Bases: deepmd.utils.plugin.PluginVariant

The abstract class for descriptors. All specific descriptors should be based on this class.

The descriptor $\mathcal{D}$ describes the environment of an atom, which should be a function of coordinates and types of its neighbour atoms.

Notes

Only methods and attributes defined in this class are generally public, that can be called by other classes.

Examples

>>> descript = Descriptor(type="se_e2_a", rcut=6., rcut_smth=0.5, sel=[50])
>>> type(descript)
<class 'deepmd.descriptor.se_a.DescrptSeA'>

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor.
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor.
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns neighbor information.
`get_ntypes`()	Returns the number of atom types.
`get_rcut`()	Returns the cut-off radius.
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(*tensors)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial.
`register`(key)	Regiester a descriptor plugin.

abstract build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: Dict[str, Any], reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor.

Parameters

coord_tf.Tensor: The coordinate of atoms
atype_tf.Tensor: The type of atoms
natomstf.Tensor: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
boxtf.Tensor: The box of frames
meshtf.Tensor: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dictdict[str, Any]: Dictionary for additional inputs
reusebool, optional: The weights in the networks should be reused when get the variable.
suffixstr, optional: Name suffix to identify this descriptor

Returns

descriptor: tf.Tensor: The output descriptor

Notes

This method must be implemented, as it’s called by other classes.

abstract compute_input_stats(data_coord: List[numpy.ndarray], data_box: List[numpy.ndarray], data_atype: List[numpy.ndarray], natoms_vec: List[numpy.ndarray], mesh: List[numpy.ndarray], input_dict: Dict[str, List[numpy.ndarray]]) → None[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coordlist[np.ndarray]: The coordinates. Can be generated by deepmd.model.model_stat.make_stat_input()
data_boxlist[np.ndarray]: The box. Can be generated by deepmd.model.model_stat.make_stat_input()
data_atypelist[np.ndarray]: The atom types. Can be generated by deepmd.model.model_stat.make_stat_input()
natoms_veclist[np.ndarray]: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.model_stat.make_stat_input()
meshlist[np.ndarray]: The mesh for neighbor searching. Can be generated by deepmd.model.model_stat.make_stat_input()
input_dictdict[str, list[np.ndarray]]: Dictionary for additional input

Notes

This method must be implemented, as it’s called by other classes.

enable_compression(min_nbor_dist: float, model_file: str = 'frozon_model.pb', table_extrapolate: float = 5.0, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = - 1, suffix: str = '') → None[source]

Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.

Parameters

min_nbor_distfloat: The nearest distance between atoms
model_filestr, default: ‘frozon_model.pb’: The original frozen model, which will be compressed by the program
table_extrapolatefloat, default: 5.: The scale of model extrapolation
table_stride_1float, default: 0.01: The uniform stride of the first table
table_stride_2float, default: 0.1: The uniform stride of the second table
check_frequencyint, default: -1: The overflow check frequency
suffixstr, optional: The suffix of the scope

Notes

This method is called by others when the descriptor supported compression.

enable_mixed_precision(mixed_prec: Optional[dict] = None) → None[source]

Reveive the mixed precision setting.

Parameters

mixed_prec: The mixed precision setting used in the embedding net

Notes

This method is called by others when the descriptor supported compression.

abstract get_dim_out() → int[source]

Returns the output dimension of this descriptor.

Returns

int: the output dimension of this descriptor

Notes

This method must be implemented, as it’s called by other classes.

get_dim_rot_mat_1() → int[source]

Returns the first dimension of the rotation matrix. The rotation is of shape dim_1 x 3

Returns

int: the first dimension of the rotation matrix

get_feed_dict(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor) → Dict[str, tensorflow.python.framework.ops.Tensor][source]

Generate the feed_dict for current descriptor

Parameters

coord_tf.Tensor: The coordinate of atoms
atype_tf.Tensor: The type of atoms
natomstf.Tensor: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
boxtf.Tensor: The box. Can be generated by deepmd.model.make_stat_input
meshtf.Tensor: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.

Returns

feed_dictdict[str, tf.Tensor]: The output feed_dict of current descriptor

get_nlist() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]][source]

Returns neighbor information.

Returns

nlisttf.Tensor: Neighbor list
rijtf.Tensor: The relative distance between the neighbor and the center atom.
sel_alist[int]: The number of neighbors with full information
sel_rlist[int]: The number of neighbors with only radial information

abstract get_ntypes() → int[source]

Returns the number of atom types.

Returns

int: the number of atom types

Notes

This method must be implemented, as it’s called by other classes.

abstract get_rcut() → float[source]

Returns the cut-off radius.

Returns

float: the cut-off radius

Notes

This method must be implemented, as it’s called by other classes.

get_tensor_names(suffix: str = '') → Tuple[str][source]

Get names of tensors.

Parameters

suffixstr: The suffix of the scope

Returns

Tuple[str]: Names of tensors

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the embedding net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr, optional: The suffix of the scope

Notes

This method is called by others when the descriptor supported initialization from the given variables.

pass_tensors_from_frz_model(*tensors: tensorflow.python.framework.ops.Tensor) → None[source]

Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def

Parameters

*tensorstf.Tensor: passed tensors

Notes

The number of parameters in the method must be equal to the numbers of returns in get_tensor_names().

abstract prod_force_virial(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Compute force and virial.

Parameters

atom_enertf.Tensor: The atomic energy
natomstf.Tensor: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns

forcetf.Tensor: The force on atoms
virialtf.Tensor: The total virial
atom_virialtf.Tensor: The atomic virial

static register(key: str) → deepmd.descriptor.descriptor.Descriptor[source]

Regiester a descriptor plugin.

Parameters

keystr: the key of a descriptor

Returns

Descriptor: the regiestered descriptor

Examples

>>> @Descriptor.register("some_descrpt")
    class SomeDescript(Descriptor):
        pass

deepmd.descriptor.hybrid module

class deepmd.descriptor.hybrid.DescrptHybrid(*args, **kwargs)[source]

Bases: deepmd.descriptor.descriptor.Descriptor

Concate a list of descriptors to form a new descriptor.

Parameters

listlist: Build a descriptor from the concatenation of the list of descriptors.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns neighbor information.
`get_nlist_i`(ii)	Get the neighbor information of the ii-th descriptor
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radius
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(*tensors)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input

enable_compression(min_nbor_dist: float, model_file: str = 'frozon_model.pb', table_extrapolate: float = 5.0, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = - 1, suffix: str = '') → None[source]

Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.

Parameters

min_nbor_distfloat: The nearest distance between atoms
model_filestr, default: ‘frozon_model.pb’: The original frozen model, which will be compressed by the program
table_extrapolatefloat, default: 5.: The scale of model extrapolation
table_stride_1float, default: 0.01: The uniform stride of the first table
table_stride_2float, default: 0.1: The uniform stride of the second table
check_frequencyint, default: -1: The overflow check frequency
suffixstr, optional: The suffix of the scope

enable_mixed_precision(mixed_prec: Optional[dict] = None) → None[source]

Reveive the mixed precision setting.

Parameters

mixed_prec: The mixed precision setting used in the embedding net

get_dim_out() → int[source]: Returns the output dimension of this descriptor

get_nlist_i(ii: int) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]][source]

Get the neighbor information of the ii-th descriptor

Parameters

iiint: The index of the descriptor

Returns

nlist: Neighbor list
rij: The relative distance between the neighbor and the center atom.
sel_a: The number of neighbors with full information
sel_r: The number of neighbors with only radial information

get_ntypes() → int[source]: Returns the number of atom types

get_rcut() → float[source]: Returns the cut-off radius

get_tensor_names(suffix: str = '') → Tuple[str][source]

Get names of tensors.

Parameters

suffixstr: The suffix of the scope

Returns

Tuple[str]: Names of tensors

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the embedding net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr, optional: The suffix of the scope

pass_tensors_from_frz_model(*tensors: tensorflow.python.framework.ops.Tensor) → None[source]

Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def

Parameters

*tensorstf.Tensor: passed tensors

prod_force_virial(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Compute force and virial

Parameters

atom_ener: The atomic energy
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns

force: The force on atoms
virial: The total virial
atom_virial: The atomic virial

deepmd.descriptor.loc_frame module

class deepmd.descriptor.loc_frame.DescrptLocFrame(*args, **kwargs)[source]

Bases: deepmd.descriptor.descriptor.Descriptor

Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.

Parameters

rcut

The cut-off radius

sel_alist[str]

The length of the list should be the same as the number of atom types in the system. sel_a[i] gives the selected number of type-i neighbors. The full relative coordinates of the neighbors are used by the descriptor.

sel_rlist[str]

The length of the list should be the same as the number of atom types in the system. sel_r[i] gives the selected number of type-i neighbors. Only relative distance of the neighbors are used by the descriptor. sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.

axis_rule: list[int]

The length should be 6 times of the number of types. - axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.

axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.
axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.
axis_rule[i*6+3]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.
axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.
axis_rule[i*6+5]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radisu
`get_rot_mat`()	Get rotational matrix
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(*tensors)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input

get_dim_out() → int[source]: Returns the output dimension of this descriptor

get_nlist() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]][source]

Returns

nlist: Neighbor list
rij: The relative distance between the neighbor and the center atom.
sel_a: The number of neighbors with full information
sel_r: The number of neighbors with only radial information

get_ntypes() → int[source]: Returns the number of atom types

get_rcut() → float[source]: Returns the cut-off radisu

get_rot_mat() → tensorflow.python.framework.ops.Tensor[source]: Get rotational matrix

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the embedding net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr, optional: The suffix of the scope

prod_force_virial(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Compute force and virial

Parameters

atom_ener: The atomic energy
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns

force: The force on atoms
virial: The total virial
atom_virial: The atomic virial

deepmd.descriptor.se module

class deepmd.descriptor.se.DescrptSe(*args, **kwargs)[source]

Bases: deepmd.descriptor.descriptor.Descriptor

A base class for smooth version of descriptors.

Notes

All of these descriptors have an environmental matrix and an embedding network (deepmd.utils.network.embedding_net()), so they can share some similiar methods without defining them twice.

Attributes

embedding_net_variablesdict: initial embedding network variables
descrpt_reshapetf.Tensor: the reshaped descriptor
descrpt_derivtf.Tensor: the descriptor derivative
rijtf.Tensor: distances between two atoms
nlisttf.Tensor: the neighbor list

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor.
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor.
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns neighbor information.
`get_ntypes`()	Returns the number of atom types.
`get_rcut`()	Returns the cut-off radius.
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(descrpt_reshape, ...)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial.
`register`(key)	Regiester a descriptor plugin.

get_tensor_names(suffix: str = '') → Tuple[str][source]

Get names of tensors.

Parameters

suffixstr: The suffix of the scope

Returns

Tuple[str]: Names of tensors

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the embedding net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr, optional: The suffix of the scope

pass_tensors_from_frz_model(descrpt_reshape: tensorflow.python.framework.ops.Tensor, descrpt_deriv: tensorflow.python.framework.ops.Tensor, rij: tensorflow.python.framework.ops.Tensor, nlist: tensorflow.python.framework.ops.Tensor)[source]

Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def

Parameters

descrpt_reshape: The passed descrpt_reshape tensor
descrpt_deriv: The passed descrpt_deriv tensor
rij: The passed rij tensor
nlist: The passed nlist tensor

property precision: tensorflow.python.framework.dtypes.DType: Precision of filter network.

deepmd.descriptor.se_a module

class deepmd.descriptor.se_a.DescrptSeA(*args, **kwargs)[source]

Bases: deepmd.descriptor.se.DescrptSe

DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes the distance between atoms as input.

The descriptor $\mathcal{D}^i \in \mathcal{R}^{M_1 \times M_2}$ is given by [1]

\[\mathcal{D}^i = (\mathcal{G}^i)^T \mathcal{R}^i (\mathcal{R}^i)^T \mathcal{G}^i_<\]

where $\mathcal{R}^i \in \mathbb{R}^{N \times 4}$ is the coordinate matrix, and each row of $\mathcal{R}^i$ can be constructed as follows

\[(\mathcal{R}^i)_j = [ \begin{array}{c} s(r_{ji}) & \frac{s(r_{ji})x_{ji}}{r_{ji}} & \frac{s(r_{ji})y_{ji}}{r_{ji}} & \frac{s(r_{ji})z_{ji}}{r_{ji}} \end{array} ]\]

where $\mathbf{R}_{ji}=\mathbf{R}_j-\mathbf{R}_i = (x_{ji}, y_{ji}, z_{ji})$ is the relative coordinate and $r_{ji}=\lVert \mathbf{R}_{ji} \lVert$ is its norm. The switching function $s(r)$ is defined as:

\[\begin{split}s(r)= \begin{cases} \frac{1}{r}, & r<r_s \\ \frac{1}{r} \{ {(\frac{r - r_s}{ r_c - r_s})}^3 (-6 {(\frac{r - r_s}{ r_c - r_s})}^2 +15 \frac{r - r_s}{ r_c - r_s} -10) +1 \}, & r_s \leq r<r_c \\ 0, & r \geq r_c \end{cases}\end{split}\]

Each row of the embedding matrix $\mathcal{G}^i \in \mathbb{R}^{N \times M_1}$ consists of outputs of a embedding network $\mathcal{N}$ of $s(r_{ji})$:

\[(\mathcal{G}^i)_j = \mathcal{N}(s(r_{ji}))\]

$\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}$ takes first $M_2$ columns of $\mathcal{G}^i$. The equation of embedding network $\mathcal{N}$ can be found at deepmd.utils.network.embedding_net().

Parameters

rcut: The cut-off radius $r_c$
rcut_smth: From where the environment matrix should be smoothed $r_s$
sellist[str]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuronlist[int]: Number of neurons in each hidden layers of the embedding net $\mathcal{N}$
axis_neuron: Number of the axis neuron $M_2$ (number of columns of the sub-matrix of the embedding matrix)
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
type_one_side: Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
exclude_typesList[List[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.
set_davg_zero: Set the shift of embedding net input to zero.
activation_function: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

References

1(1,2): Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, and E. Weinan. 2018. End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 4441–4451.

Attributes

precision: Precision of filter network.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radius
`get_rot_mat`()	Get rotational matrix
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(descrpt_reshape, ...)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input

enable_compression(min_nbor_dist: float, model_file: str = 'frozon_model.pb', table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = - 1, suffix: str = '') → None[source]

Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.

Parameters

min_nbor_dist: The nearest distance between atoms
model_file: The original frozen model, which will be compressed by the program
table_extrapolate: The scale of model extrapolation
table_stride_1: The uniform stride of the first table
table_stride_2: The uniform stride of the second table
check_frequency: The overflow check frequency
suffixstr, optional: The suffix of the scope

enable_mixed_precision(mixed_prec: Optional[dict] = None) → None[source]

Reveive the mixed precision setting.

Parameters

mixed_prec: The mixed precision setting used in the embedding net

get_dim_out() → int[source]: Returns the output dimension of this descriptor

get_dim_rot_mat_1() → int[source]: Returns the first dimension of the rotation matrix. The rotation is of shape dim_1 x 3

get_nlist() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]][source]

Returns

nlist: Neighbor list
rij: The relative distance between the neighbor and the center atom.
sel_a: The number of neighbors with full information
sel_r: The number of neighbors with only radial information

get_ntypes() → int[source]: Returns the number of atom types

get_rcut() → float[source]: Returns the cut-off radius

get_rot_mat() → tensorflow.python.framework.ops.Tensor[source]: Get rotational matrix

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the embedding net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr, optional: The suffix of the scope

prod_force_virial(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Compute force and virial

Parameters

atom_ener: The atomic energy
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns

force: The force on atoms
virial: The total virial
atom_virial: The atomic virial

deepmd.descriptor.se_a_ebd module

class deepmd.descriptor.se_a_ebd.DescrptSeAEbd(*args, **kwargs)[source]

Bases: deepmd.descriptor.se_a.DescrptSeA

DeepPot-SE descriptor with type embedding approach.

Parameters

rcut: The cut-off radius
rcut_smth: From where the environment matrix should be smoothed
sellist[str]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuronlist[int]: Number of neurons in each hidden layers of the embedding net
axis_neuron: Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
type_one_side: Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
type_nchanl: Number of channels for type representation
type_nlayer: Number of hidden layers for the type embedding net (skip connected).
numb_aparam: Number of atomic parameters. If >0 it will be embedded with atom types.
set_davg_zero: Set the shift of embedding net input to zero.
activation_function: The activation function in the embedding net. Supported options are {0}
precision: The precision of the embedding net parameters. Supported options are {1}
exclude_typesList[List[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

Attributes

precision: Precision of filter network.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radius
`get_rot_mat`()	Get rotational matrix
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(descrpt_reshape, ...)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

deepmd.descriptor.se_a_ef module

class deepmd.descriptor.se_a_ef.DescrptSeAEf(*args, **kwargs)[source]

Bases: deepmd.descriptor.descriptor.Descriptor

Parameters

rcut: The cut-off radius
rcut_smth: From where the environment matrix should be smoothed
sellist[str]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuronlist[int]: Number of neurons in each hidden layers of the embedding net
axis_neuron: Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
type_one_side: Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
exclude_typesList[List[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.
set_davg_zero: Set the shift of embedding net input to zero.
activation_function: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radisu
`get_rot_mat`()	Get rotational matrix
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(*tensors)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs. Should have ‘efield’.
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input

get_dim_out() → int[source]: Returns the output dimension of this descriptor

get_dim_rot_mat_1() → int[source]: Returns the first dimension of the rotation matrix. The rotation is of shape dim_1 x 3

get_nlist() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]][source]

Returns

nlist: Neighbor list
rij: The relative distance between the neighbor and the center atom.
sel_a: The number of neighbors with full information
sel_r: The number of neighbors with only radial information

get_ntypes() → int[source]: Returns the number of atom types

get_rcut() → float[source]: Returns the cut-off radisu

get_rot_mat() → tensorflow.python.framework.ops.Tensor[source]: Get rotational matrix

prod_force_virial(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Compute force and virial

Parameters

atom_ener: The atomic energy
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns

force: The force on atoms
virial: The total virial
atom_virial: The atomic virial

class deepmd.descriptor.se_a_ef.DescrptSeAEfLower(*args, **kwargs)[source]

Bases: deepmd.descriptor.se_a.DescrptSeA

Helper class for implementing DescrptSeAEf

Attributes

precision: Precision of filter network.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radius
`get_rot_mat`()	Get rotational matrix
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(descrpt_reshape, ...)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_, atype_, natoms, box_, mesh, input_dict, suffix='', reuse=None)[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh, input_dict)[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input

deepmd.descriptor.se_atten module

class deepmd.descriptor.se_atten.DescrptSeAtten(*args, **kwargs)[source]

Bases: deepmd.descriptor.se_a.DescrptSeA

Parameters

rcut: The cut-off radius $r_c$
rcut_smth: From where the environment matrix should be smoothed $r_s$
sellist[str]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuronlist[int]: Number of neurons in each hidden layers of the embedding net $\mathcal{N}$
axis_neuron: Number of the axis neuron $M_2$ (number of columns of the sub-matrix of the embedding matrix)
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
type_one_side: Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
exclude_typesList[List[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.
set_davg_zero: Set the shift of embedding net input to zero.
activation_function: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
attn: The length of hidden vector during scale-dot attention computation.
attn_layer: The number of layers in attention mechanism.
attn_dotr: Whether to dot the relative coordinates on the attention weights as a gated scheme.
attn_mask: Whether to mask the diagonal in the attention weights.

Attributes

precision: Precision of filter network.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radius
`get_rot_mat`()	Get rotational matrix
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(descrpt_reshape, ...)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict, mixed_type: bool = False, real_natoms_vec: Optional[list] = None) → None[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. If mixed_type is True, this para is blank. See real_natoms_vec.
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.
real_natoms_vec: If mixed_type is True, it takes in the real natoms_vec for each frame.

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the embedding net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr, optional: The suffix of the scope

deepmd.descriptor.se_r module

class deepmd.descriptor.se_r.DescrptSeR(*args, **kwargs)[source]

Bases: deepmd.descriptor.se.DescrptSe

DeepPot-SE constructed from radial information of atomic configurations.

The embedding takes the distance between atoms as input.

Parameters

rcut: The cut-off radius
rcut_smth: From where the environment matrix should be smoothed
sellist[str]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuronlist[int]: Number of neurons in each hidden layers of the embedding net
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
type_one_side: Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
exclude_typesList[List[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.
activation_function: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Attributes

precision: Precision of filter network.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radisu
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(descrpt_reshape, ...)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh, input_dict)[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input

enable_compression(min_nbor_dist: float, model_file: str = 'frozon_model.pb', table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = - 1, suffix: str = '') → None[source]

Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.

Parameters

min_nbor_dist: The nearest distance between atoms
model_file: The original frozen model, which will be compressed by the program
table_extrapolate: The scale of model extrapolation
table_stride_1: The uniform stride of the first table
table_stride_2: The uniform stride of the second table
check_frequency: The overflow check frequency
suffixstr, optional: The suffix of the scope

get_dim_out()[source]: Returns the output dimension of this descriptor

get_nlist()[source]

Returns

nlist: Neighbor list
rij: The relative distance between the neighbor and the center atom.
sel_a: The number of neighbors with full information
sel_r: The number of neighbors with only radial information

get_ntypes()[source]: Returns the number of atom types

get_rcut()[source]: Returns the cut-off radisu

prod_force_virial(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Compute force and virial

Parameters

atom_ener: The atomic energy
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns

force: The force on atoms
virial: The total virial
atom_virial: The atomic virial

deepmd.descriptor.se_t module

class deepmd.descriptor.se_t.DescrptSeT(*args, **kwargs)[source]

Bases: deepmd.descriptor.se.DescrptSe

DeepPot-SE constructed from all information (both angular and radial) of atomic configurations.

The embedding takes angles between two neighboring atoms as input.

Parameters

rcut: The cut-off radius
rcut_smth: From where the environment matrix should be smoothed
sellist[str]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuronlist[int]: Number of neurons in each hidden layers of the embedding net
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
set_davg_zero: Set the shift of embedding net input to zero.
activation_function: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Attributes

precision: Precision of filter network.

Methods

`build`(coord_, atype_, natoms, box_, mesh, ...)	Build the computational graph for the descriptor
`compute_input_stats`(data_coord, data_box, ...)	Compute the statisitcs (avg and std) of the training data.
`enable_compression`(min_nbor_dist[, ...])	Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_dim_out`()	Returns the output dimension of this descriptor
`get_dim_rot_mat_1`()	Returns the first dimension of the rotation matrix.
`get_feed_dict`(coord_, atype_, natoms, box, mesh)	Generate the feed_dict for current descriptor
`get_nlist`()	Returns
`get_ntypes`()	Returns the number of atom types
`get_rcut`()	Returns the cut-off radisu
`get_tensor_names`([suffix])	Get names of tensors.
`init_variables`(graph, graph_def[, suffix])	Init the embedding net variables with the given dict
`pass_tensors_from_frz_model`(descrpt_reshape, ...)	Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
`prod_force_virial`(atom_ener, natoms)	Compute force and virial
`register`(key)	Regiester a descriptor plugin.

build(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for the descriptor

Parameters

coord_: The coordinate of atoms
atype_: The type of atoms
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
input_dict: Dictionary for additional inputs
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

descriptor: The output descriptor

compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None[source]

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters

data_coord: The coordinates. Can be generated by deepmd.model.make_stat_input
data_box: The box. Can be generated by deepmd.model.make_stat_input
data_atype: The atom types. Can be generated by deepmd.model.make_stat_input
natoms_vec: The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
mesh: The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
input_dict: Dictionary for additional input

enable_compression(min_nbor_dist: float, model_file: str = 'frozon_model.pb', table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = - 1, suffix: str = '') → None[source]

Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.

Parameters

min_nbor_dist: The nearest distance between atoms
model_file: The original frozen model, which will be compressed by the program
table_extrapolate: The scale of model extrapolation
table_stride_1: The uniform stride of the first table
table_stride_2: The uniform stride of the second table
check_frequency: The overflow check frequency
suffixstr, optional: The suffix of the scope

get_dim_out() → int[source]: Returns the output dimension of this descriptor

get_nlist() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]][source]

Returns

nlist: Neighbor list
rij: The relative distance between the neighbor and the center atom.
sel_a: The number of neighbors with full information
sel_r: The number of neighbors with only radial information

get_ntypes() → int[source]: Returns the number of atom types

get_rcut() → float[source]: Returns the cut-off radisu

prod_force_virial(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Compute force and virial

Parameters

atom_ener: The atomic energy
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns

force: The force on atoms
virial: The total virial
atom_virial: The atomic virial

deepmd.entrypoints package

Submodule that contains all the DeePMD-Kit entry point scripts.

deepmd.entrypoints.compress(*, input: str, output: str, extrapolate: int, step: float, frequency: str, checkpoint_folder: str, training_script: str, mpi_log: str, log_path: Optional[str], log_level: int, **kwargs)[source]

Compress model.

The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first table takes the step parameter as the domain’s uniform step size, while the second table takes 10 * step as it’s uniform step size. The range of the first table is automatically detected by the code, while the second table ranges from the first table’s upper boundary(upper) to the extrapolate(parameter) * upper.

Parameters

inputstr: frozen model file to compress
outputstr: compressed model filename
extrapolateint: scale of model extrapolation
stepfloat: uniform step size of the tabulation’s first table
frequencystr: frequency of tabulation overflow check
checkpoint_folderstr: trining checkpoint folder for freezing
training_scriptstr: training script of the input frozen model
mpi_logstr: mpi logging mode for training
log_pathOptional[str]: if speccified log will be written to this file
log_levelint: logging level

deepmd.entrypoints.config(*, output: str, **kwargs)[source]

Auto config file generator.

Parameters

output: str: file to write config file

Raises

RuntimeError: if user does not input any systems
ValueError: if output file is of wrong type

deepmd.entrypoints.convert(*, FROM: str, input_model: str, output_model: str, **kwargs)[source]

deepmd.entrypoints.doc_train_input(*, out_type: str = 'rst', **kwargs)[source]: Print out trining input arguments to console.

deepmd.entrypoints.freeze(*, checkpoint_folder: str, output: str, node_names: Optional[str] = None, nvnmd_weight: Optional[str] = None, **kwargs)[source]

Freeze the graph in supplied folder.

Parameters

checkpoint_folderstr: location of the folder with model
outputstr: output file name
node_namesOptional[str], optional: names of nodes to output, by default None

deepmd.entrypoints.make_model_devi(*, models: list, system: str, set_prefix: str, output: str, frequency: int, **kwargs)[source]

Make model deviation calculation

Parameters

models: list: A list of paths of models to use for making model deviation
system: str: The path of system to make model deviation calculation
set_prefix: str: The set prefix of the system
output: str: The output file for model deviation results
frequency: int: The number of steps that elapse between writing coordinates in a trajectory by a MD engine (such as Gromacs / Lammps). This paramter is used to determine the index in the output file.

deepmd.entrypoints.neighbor_stat(*, system: str, rcut: float, type_map: List[str], one_type: bool = False, **kwargs)[source]

Calculate neighbor statistics.

Parameters

systemstr: system to stat
rcutfloat: cutoff radius
type_maplist[str]: type map
one_typebool, optional, default=False: treat all types as a single type

Examples

>>> neighbor_stat(system='.', rcut=6., type_map=["C", "H", "O", "N", "P", "S", "Mg", "Na", "HW", "OW", "mNa", "mCl", "mC", "mH", "mMg", "mN", "mO", "mP"])
min_nbor_dist: 0.6599510670195264
max_nbor_size: [23, 26, 19, 16, 2, 2, 1, 1, 72, 37, 5, 0, 31, 29, 1, 21, 20, 5]

deepmd.entrypoints.test(*, model: str, system: str, set_prefix: str, numb_test: int, rand_seed: Optional[int], shuffle_test: bool, detail_file: str, atomic: bool, **kwargs)[source]

Test model predictions.

Parameters

modelstr: path where model is stored
systemstr: system directory
set_prefixstr: string prefix of set
numb_testint: munber of tests to do
rand_seedOptional[int]: seed for random generator
shuffle_testbool: whether to shuffle tests
detail_fileOptional[str]: file where test details will be output
atomicbool: whether per atom quantities should be computed

Raises

RuntimeError: if no valid system was found

deepmd.entrypoints.train_dp(*, INPUT: str, init_model: Optional[str], restart: Optional[str], output: str, init_frz_model: str, mpi_log: str, log_level: int, log_path: Optional[str], is_compress: bool = False, skip_neighbor_stat: bool = False, **kwargs)

Run DeePMD model training.

Parameters

INPUTstr: json/yaml control file
init_modelOptional[str]: path to checkpoint folder or None
restartOptional[str]: path to checkpoint folder or None
outputstr: path for dump file with arguments
init_frz_modelstr: path to frozen model or None
mpi_logstr: mpi logging mode
log_levelint: logging level defined by int 0-3
log_pathOptional[str]: logging file path or None if logs are to be output only to stdout
is_compress: bool: indicates whether in the model compress mode
skip_neighbor_statbool, default=False: skip checking neighbor statistics

Raises

RuntimeError: if distributed training job nem is wrong

deepmd.entrypoints.transfer(*, old_model: str, raw_model: str, output: str, **kwargs)[source]

Transfer operation from old fron graph to new prepared raw graph.

Parameters

old_modelstr: frozen old graph model
raw_modelstr: new model that will accept ops from old model
outputstr: new model with transfered parameters will be saved to this location

Submodules

deepmd.entrypoints.compress module

Compress a model, which including tabulating the embedding-net.

deepmd.entrypoints.compress.compress(*, input: str, output: str, extrapolate: int, step: float, frequency: str, checkpoint_folder: str, training_script: str, mpi_log: str, log_path: Optional[str], log_level: int, **kwargs)[source]

Compress model.

The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first table takes the step parameter as the domain’s uniform step size, while the second table takes 10 * step as it’s uniform step size. The range of the first table is automatically detected by the code, while the second table ranges from the first table’s upper boundary(upper) to the extrapolate(parameter) * upper.

Parameters

inputstr: frozen model file to compress
outputstr: compressed model filename
extrapolateint: scale of model extrapolation
stepfloat: uniform step size of the tabulation’s first table
frequencystr: frequency of tabulation overflow check
checkpoint_folderstr: trining checkpoint folder for freezing
training_scriptstr: training script of the input frozen model
mpi_logstr: mpi logging mode for training
log_pathOptional[str]: if speccified log will be written to this file
log_levelint: logging level

deepmd.entrypoints.config module

Quickly create a configuration file for smooth model.

deepmd.entrypoints.config.config(*, output: str, **kwargs)[source]

Auto config file generator.

Parameters

output: str: file to write config file

Raises

RuntimeError: if user does not input any systems
ValueError: if output file is of wrong type

deepmd.entrypoints.convert module

deepmd.entrypoints.convert.convert(*, FROM: str, input_model: str, output_model: str, **kwargs)[source]

deepmd.entrypoints.doc module

Module that prints train input arguments docstrings.

deepmd.entrypoints.doc.doc_train_input(*, out_type: str = 'rst', **kwargs)[source]: Print out trining input arguments to console.

deepmd.entrypoints.freeze module

Script for freezing TF trained graph so it can be used with LAMMPS and i-PI.

References

https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc

deepmd.entrypoints.freeze.freeze(*, checkpoint_folder: str, output: str, node_names: Optional[str] = None, nvnmd_weight: Optional[str] = None, **kwargs)[source]

Freeze the graph in supplied folder.

Parameters

checkpoint_folderstr: location of the folder with model
outputstr: output file name
node_namesOptional[str], optional: names of nodes to output, by default None

deepmd.entrypoints.main module

DeePMD-Kit entry point module.

deepmd.entrypoints.main.get_ll(log_level: str) → int[source]

Convert string to python logging level.

Parameters

log_levelstr: allowed input values are: DEBUG, INFO, WARNING, ERROR, 3, 2, 1, 0

Returns

int: one of python logging module log levels - 10, 20, 30 or 40

deepmd.entrypoints.main.main()[source]

DeePMD-Kit entry point.

Raises

RuntimeError: if no command was input

deepmd.entrypoints.main.main_parser() → argparse.ArgumentParser[source]

DeePMD-Kit commandline options argument parser.

Returns

argparse.ArgumentParser: main parser of DeePMD-kit

deepmd.entrypoints.main.parse_args(args: Optional[List[str]] = None) → argparse.Namespace[source]

Parse arguments and convert argument strings to objects.

Parameters

args: List[str]: list of command line arguments, main purpose is testing default option None takes arguments from sys.argv

Returns

argparse.Namespace: the populated namespace

deepmd.entrypoints.neighbor_stat module

deepmd.entrypoints.neighbor_stat.neighbor_stat(*, system: str, rcut: float, type_map: List[str], one_type: bool = False, **kwargs)[source]

Calculate neighbor statistics.

Parameters

systemstr: system to stat
rcutfloat: cutoff radius
type_maplist[str]: type map
one_typebool, optional, default=False: treat all types as a single type

Examples

>>> neighbor_stat(system='.', rcut=6., type_map=["C", "H", "O", "N", "P", "S", "Mg", "Na", "HW", "OW", "mNa", "mCl", "mC", "mH", "mMg", "mN", "mO", "mP"])
min_nbor_dist: 0.6599510670195264
max_nbor_size: [23, 26, 19, 16, 2, 2, 1, 1, 72, 37, 5, 0, 31, 29, 1, 21, 20, 5]

deepmd.entrypoints.test module

Test trained DeePMD model.

deepmd.entrypoints.test.test(*, model: str, system: str, set_prefix: str, numb_test: int, rand_seed: Optional[int], shuffle_test: bool, detail_file: str, atomic: bool, **kwargs)[source]

Test model predictions.

Parameters

modelstr: path where model is stored
systemstr: system directory
set_prefixstr: string prefix of set
numb_testint: munber of tests to do
rand_seedOptional[int]: seed for random generator
shuffle_testbool: whether to shuffle tests
detail_fileOptional[str]: file where test details will be output
atomicbool: whether per atom quantities should be computed

Raises

RuntimeError: if no valid system was found

deepmd.entrypoints.train module

DeePMD training entrypoint script.

Can handle local or distributed training.

deepmd.entrypoints.train.train(*, INPUT: str, init_model: Optional[str], restart: Optional[str], output: str, init_frz_model: str, mpi_log: str, log_level: int, log_path: Optional[str], is_compress: bool = False, skip_neighbor_stat: bool = False, **kwargs)[source]

Run DeePMD model training.

Parameters

INPUTstr: json/yaml control file
init_modelOptional[str]: path to checkpoint folder or None
restartOptional[str]: path to checkpoint folder or None
outputstr: path for dump file with arguments
init_frz_modelstr: path to frozen model or None
mpi_logstr: mpi logging mode
log_levelint: logging level defined by int 0-3
log_pathOptional[str]: logging file path or None if logs are to be output only to stdout
is_compress: bool: indicates whether in the model compress mode
skip_neighbor_statbool, default=False: skip checking neighbor statistics

Raises

RuntimeError: if distributed training job nem is wrong

deepmd.entrypoints.transfer module

Module used for transfering parameters between models.

deepmd.entrypoints.transfer.transfer(*, old_model: str, raw_model: str, output: str, **kwargs)[source]

Transfer operation from old fron graph to new prepared raw graph.

Parameters

old_modelstr: frozen old graph model
raw_modelstr: new model that will accept ops from old model
outputstr: new model with transfered parameters will be saved to this location

deepmd.fit package

Submodules

deepmd.fit.dipole module

class deepmd.fit.dipole.DipoleFittingSeA(descrpt: tensorflow.python.framework.ops.Tensor, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, sel_type: Optional[List[int]] = None, seed: Optional[int] = None, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False)[source]

Bases: deepmd.fit.fitting.Fitting

Fit the atomic dipole with descriptor se_a

Parameters

descrpttf.Tensor: The descrptor
neuronList[int]: Number of neurons in each hidden layer of the fitting net
resnet_dtbool: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
sel_typeList[int]: The atom types selected to have an atomic dipole prediction. If is None, all atoms are selected.
seedint: Random seed for initializing the network parameters.
activation_functionstr: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precisionstr: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Attributes

precision: Precision of fitting network.

Methods

`build`(input_d, rot_mat, natoms[, reuse, suffix])	Build the computational graph for fitting net
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_out_size`()	Get the output size.
`get_sel_type`()	Get selected type
`init_variables`(graph, graph_def[, suffix])	Init the fitting net variables with the given dict

build(input_d: tensorflow.python.framework.ops.Tensor, rot_mat: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, reuse: bool = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for fitting net

Parameters

input_d: The input descriptor
rot_mat: The rotation matrix from the descriptor.
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

dipole: The atomic dipole.

enable_mixed_precision(mixed_prec: Optional[dict] = None) → None[source]

Reveive the mixed precision setting.

Parameters

mixed_prec: The mixed precision setting used in the embedding net

get_out_size() → int[source]: Get the output size. Should be 3

get_sel_type() → int[source]: Get selected type

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the fitting net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr: suffix to name scope

deepmd.fit.ener module

class deepmd.fit.ener.EnerFitting(descrpt: tensorflow.python.framework.ops.Tensor, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, rcond: float = 0.001, tot_ener_zero: bool = False, trainable: Optional[List[bool]] = None, seed: Optional[int] = None, atom_ener: List[float] = [], activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False)[source]

Bases: deepmd.fit.fitting.Fitting

Fitting the energy of the system. The force and the virial can also be trained.

The potential energy $E$ is a fitting network function of the descriptor $\mathcal{D}$:

\[E(\mathcal{D}) = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)} \circ \cdots \circ \mathcal{L}^{(1)} \circ \mathcal{L}^{(0)}\]

The first $n$ hidden layers $\mathcal{L}^{(0)}, \cdots, \mathcal{L}^{(n-1)}$ are given by

\[\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b})\]

where $\mathbf{x} \in \mathbb{R}^{N_1}$ is the input vector and $\mathbf{y} \in \mathbb{R}^{N_2}$ is the output vector. $\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}$ and $\mathbf{b} \in \mathbb{R}^{N_2}$ are weights and biases, respectively, both of which are trainable if trainable[i] is True. $\boldsymbol{\phi}$ is the activation function.

The output layer $\mathcal{L}^{(n)}$ is given by

\[\mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})= \mathbf{x}^T\mathbf{w}+\mathbf{b}\]

where $\mathbf{x} \in \mathbb{R}^{N_{n-1}}$ is the input vector and $\mathbf{y} \in \mathbb{R}$ is the output scalar. $\mathbf{w} \in \mathbb{R}^{N_{n-1}}$ and $\mathbf{b} \in \mathbb{R}$ are weights and bias, respectively, both of which are trainable if trainable[n] is True.

Parameters

descrpt: The descrptor $\mathcal{D}$
neuron: Number of neurons $N$ in each hidden layer of the fitting net
resnet_dt: Time-step dt in the resnet construction: $y = x + dt * \phi (Wx + b)$
numb_fparam: Number of frame parameter
numb_aparam: Number of atomic parameter
rcond: The condition number for the regression of atomic energy.
tot_ener_zero: Force the total energy to zero. Useful for the charge fitting.
trainable: If the weights of fitting net are trainable. Suppose that we have $N_l$ hidden layers in the fitting net, this list is of length $N_l + 1$, specifying if the hidden layers and the output layer are trainable.
seed: Random seed for initializing the network parameters.
atom_ener: Specifying atomic energy contribution in vacuum. The set_davg_zero key in the descrptor should be set.
activation_function: The activation function $\boldsymbol{\phi}$ in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Attributes

precision: Precision of fitting network.

Methods

`build`(inputs, natoms[, input_dict, reuse, ...])	Build the computational graph for fitting net
`compute_input_stats`(all_stat[, protection])	Compute the input statistics
`compute_output_stats`(all_stat[, mixed_type])	Compute the ouput statistics
`enable_compression`(model_file[, suffix])	Set the fitting net attributes from the frozen model_file when fparam or aparam is not zero
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_numb_aparam`()	Get the number of atomic parameters
`get_numb_fparam`()	Get the number of frame parameters
`init_variables`(graph, graph_def[, suffix])	Init the fitting net variables with the given dict

build(inputs: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, input_dict: dict = None, reuse: bool = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for fitting net

Parameters

inputs: The input descriptor
input_dict: Additional dict for inputs. if numb_fparam > 0, should have input_dict[‘fparam’] if numb_aparam > 0, should have input_dict[‘aparam’]
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

ener: The system energy

compute_input_stats(all_stat: dict, protection: float = 0.01) → None[source]

Compute the input statistics

Parameters

all_stat: if numb_fparam > 0 must have all_stat[‘fparam’] if numb_aparam > 0 must have all_stat[‘aparam’] can be prepared by model.make_stat_input
protection: Divided-by-zero protection

compute_output_stats(all_stat: dict, mixed_type: bool = False) → None[source]

Compute the ouput statistics

Parameters

all_stat: must have the following components: all_stat[‘energy’] of shape n_sys x n_batch x n_frame can be prepared by model.make_stat_input
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

enable_compression(model_file: str, suffix: str = '') → None[source]

Set the fitting net attributes from the frozen model_file when fparam or aparam is not zero

Parameters

model_filestr: The input frozen model file
suffixstr, optional: The suffix of the scope

enable_mixed_precision(mixed_prec: Optional[dict] = None) → None[source]

Reveive the mixed precision setting.

Parameters

mixed_prec: The mixed precision setting used in the embedding net

get_numb_aparam() → int[source]: Get the number of atomic parameters

get_numb_fparam() → int[source]: Get the number of frame parameters

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the fitting net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr: suffix to name scope

deepmd.fit.fitting module

class deepmd.fit.fitting.Fitting[source]

Bases: object

Attributes

precision: Precision of fitting network.

Methods

init_variables(graph, graph_def[, suffix])

Init the fitting net variables with the given dict

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the fitting net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr: suffix to name scope

Notes

This method is called by others when the fitting supported initialization from the given variables.

property precision: tensorflow.python.framework.dtypes.DType: Precision of fitting network.

deepmd.fit.polar module

class deepmd.fit.polar.GlobalPolarFittingSeA(descrpt: tensorflow.python.framework.ops.Tensor, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, sel_type: Optional[List[int]] = None, fit_diag: bool = True, scale: Optional[List[float]] = None, diag_shift: Optional[List[float]] = None, seed: Optional[int] = None, activation_function: str = 'tanh', precision: str = 'default')[source]

Bases: object

Fit the system polarizability with descriptor se_a

Parameters

descrpttf.Tensor: The descrptor
neuronList[int]: Number of neurons in each hidden layer of the fitting net
resnet_dtbool: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
sel_typeList[int]: The atom types selected to have an atomic polarizability prediction
fit_diagbool: Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
scaleList[float]: The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
diag_shiftList[float]: The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.
seedint: Random seed for initializing the network parameters.
activation_functionstr: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precisionstr: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.

Methods

`build`(input_d, rot_mat, natoms[, reuse, suffix])	Build the computational graph for fitting net
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_out_size`()	Get the output size.
`get_sel_type`()	Get selected atom types
`init_variables`(graph, graph_def[, suffix])	Init the fitting net variables with the given dict

build(input_d, rot_mat, natoms, reuse=None, suffix='') → tensorflow.python.framework.ops.Tensor[source]

Build the computational graph for fitting net

Parameters

input_d: The input descriptor
rot_mat: The rotation matrix from the descriptor.
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

polar: The system polarizability

enable_mixed_precision(mixed_prec: Optional[dict] = None) → None[source]

Reveive the mixed precision setting.

Parameters

mixed_prec: The mixed precision setting used in the embedding net

get_out_size() → int[source]: Get the output size. Should be 9

get_sel_type() → int[source]: Get selected atom types

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the fitting net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr: suffix to name scope

class deepmd.fit.polar.PolarFittingLocFrame(jdata, descrpt)[source]

Bases: object

Fitting polarizability with local frame descriptor.

Deprecated since version 2.0.0: This class is not supported any more.

Methods

build
get_out_size
get_sel_type

build(input_d, rot_mat, natoms, reuse=None, suffix='')[source]

get_out_size()[source]

get_sel_type()[source]

class deepmd.fit.polar.PolarFittingSeA(descrpt: tensorflow.python.framework.ops.Tensor, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, sel_type: Optional[List[int]] = None, fit_diag: bool = True, scale: Optional[List[float]] = None, shift_diag: bool = True, seed: Optional[int] = None, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False)[source]

Bases: deepmd.fit.fitting.Fitting

Fit the atomic polarizability with descriptor se_a

Parameters

descrpttf.Tensor: The descrptor
neuronList[int]: Number of neurons in each hidden layer of the fitting net
resnet_dtbool: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
sel_typeList[int]: The atom types selected to have an atomic polarizability prediction. If is None, all atoms are selected.
fit_diagbool: Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
scaleList[float]: The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
diag_shiftList[float]: The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.
seedint: Random seed for initializing the network parameters.
activation_functionstr: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precisionstr: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

Attributes

precision: Precision of fitting network.

Methods

`build`(input_d, rot_mat, natoms[, reuse, suffix])	Build the computational graph for fitting net
`compute_input_stats`(all_stat[, protection])	Compute the input statistics
`enable_mixed_precision`([mixed_prec])	Reveive the mixed precision setting.
`get_out_size`()	Get the output size.
`get_sel_type`()	Get selected atom types
`init_variables`(graph, graph_def[, suffix])	Init the fitting net variables with the given dict

build(input_d: tensorflow.python.framework.ops.Tensor, rot_mat: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, reuse: bool = None, suffix: str = '')[source]

Build the computational graph for fitting net

Parameters

input_d: The input descriptor
rot_mat: The rotation matrix from the descriptor.
natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

atomic_polar: The atomic polarizability

compute_input_stats(all_stat, protection=0.01)[source]

Compute the input statistics

Parameters

all_stat: Dictionary of inputs. can be prepared by model.make_stat_input
protection: Divided-by-zero protection

enable_mixed_precision(mixed_prec: Optional[dict] = None) → None[source]

Reveive the mixed precision setting.

Parameters

mixed_prec: The mixed precision setting used in the embedding net

get_out_size() → int[source]: Get the output size. Should be 9

get_sel_type() → List[int][source]: Get selected atom types

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → None[source]

Init the fitting net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffixstr: suffix to name scope

deepmd.fit.wfc module

class deepmd.fit.wfc.WFCFitting(jdata, descrpt)[source]

Bases: object

Fitting Wannier function centers (WFCs) with local frame descriptor.

Deprecated since version 2.0.0: This class is not supported any more.

Methods

build
get_out_size
get_sel_type
get_wfc_numb

build(input_d, rot_mat, natoms, reuse=None, suffix='')[source]

get_out_size()[source]

get_sel_type()[source]

get_wfc_numb()[source]

deepmd.infer package

Submodule containing all the implemented potentials.

class deepmd.infer.DeepDipole(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = False)[source]

Bases: object

Common methods for DeepPot, DeepWFC, DeepPolar, …

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: False: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

load_prefix: str

make_natoms_vec(atom_types: numpy.ndarray) → numpy.ndarray[source]

Make the natom vector used by deepmd-kit.

Parameters

atom_types: The type of atoms

Returns

natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

property model_type: str

Get type of model.

:type:str

property model_version: str

Get version of model.

Returns

str: version of model

static reverse_map(vec: numpy.ndarray, imap: List[int]) → numpy.ndarray[source]

Reverse mapping of a vector according to the index map

Parameters

vec: Input vector. Be of shape [nframes, natoms, -1]
imap: Index map. Be of shape [natoms]

Returns

vec_out: Reverse mapped vector.

property sess: tensorflow.python.client.session.Session: Get TF session.

static sort_input(coord: numpy.ndarray, atom_type: numpy.ndarray, sel_atoms: Optional[List[int]] = None)[source]

Sort atoms in the system according their types.

Parameters

coord: The coordinates of atoms. Should be of shape [nframes, natoms, 3]
atom_type: The type of atoms Should be of shape [natoms]
sel_atom: The selected atoms by type

Returns

coord_out: The coordinates after sorting
atom_type_out: The atom types after sorting
idx_map: The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]
sel_atom_type: Only output if sel_atoms is not None The sorted selected atom types
sel_idx_map: Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.

class deepmd.infer.DeepGlobalPolar(model_file: str, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filestr: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) → numpy.ndarray[source]

Evaluate the model.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
atomic: Not used in this model
fparam: Not used in this model
aparam: Not used in this model
efield: Not used in this model

Returns

tensor: The returned tensor If atomic == False then of size nframes x variable_dof else of size nframes x natoms x variable_dof

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = True)[source]

Bases: deepmd.infer.deep_eval.DeepEval

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Examples

>>> from deepmd.infer import DeepPot
>>> import numpy as np
>>> dp = DeepPot('graph.pb')
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> e, f, v = dp.eval(coord, cell, atype)

where e, f and v are predicted energy, force and virial of the system, respectively.

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the energy, force and virial by using this DP.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`get_dim_aparam`()	Get the number (dimension) of atomic parameters of this DP.
`get_dim_fparam`()	Get the number (dimension) of frame parameters of this DP.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Unsupported in this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) → Tuple[numpy.ndarray, ...][source]

Evaluate the energy, force and virial by using this DP.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
atomic: Calculate the atomic energy and virial
fparam: The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam: The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
efield: The external field on atoms. The array should be of size nframes x natoms x 3

Returns

energy: The system energy.
force: The force on each atom
virial: The virial
atom_energy: The atomic energy. Only returned when atomic == True
atom_virial: The atomic virial. Only returned when atomic == True

eval_descriptor(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) → numpy.array[source]

Evaluate descriptors by using this DP.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
fparam: The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam: The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
efield: The external field on atoms. The array should be of size nframes x natoms x 3

Returns

descriptor: Descriptors.

get_dim_aparam() → int[source]: Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int[source]: Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int[source]: Get the number of atom types of this model.

get_rcut() → float[source]: Get the cut-off radius of this model.

get_sel_type() → List[int][source]: Unsupported in this model.

get_type_map() → List[int][source]: Get the type map (element name of the atom types) of this model.

load_prefix: str

deepmd.infer.DeepPotential(model_file: Union[str, pathlib.Path], load_prefix: str = 'load', default_tf_graph: bool = False) → Union[deepmd.infer.deep_dipole.DeepDipole, deepmd.infer.deep_polar.DeepGlobalPolar, deepmd.infer.deep_polar.DeepPolar, deepmd.infer.deep_pot.DeepPot, deepmd.infer.deep_wfc.DeepWFC][source]

Factory function that will inialize appropriate potential read from model_file.

Parameters

model_file: str: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Returns

Union[DeepDipole, DeepGlobalPolar, DeepPolar, DeepPot, DeepWFC]: one of the available potentials

Raises

RuntimeError: if model file does not correspond to any implementd potential

class deepmd.infer.DeepWFC(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.DipoleChargeModifier(model_name: str, model_charge_map: List[float], sys_charge_map: List[float], ewald_h: float = 1, ewald_beta: float = 1)[source]

Bases: deepmd.infer.deep_dipole.DeepDipole

Parameters

model_name: The model file for the DeepDipole model
model_charge_map: Gives the amount of charge for the wfcc
sys_charge_map: Gives the amount of charge for the real atoms
ewald_h: Grid spacing of the reciprocal part of Ewald sum. Unit: A
ewald_beta: Splitting parameter of the Ewald sum. Unit: A^{-1}

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`build_fv_graph`()	Build the computational graph for the force and virial inference.
`eval`(coord, box, atype[, eval_fv])	Evaluate the modification
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`modify_data`(data)	Modify data.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

build_fv_graph() → tensorflow.python.framework.ops.Tensor[source]: Build the computational graph for the force and virial inference.

eval(coord: numpy.ndarray, box: numpy.ndarray, atype: numpy.ndarray, eval_fv: bool = True) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Evaluate the modification

Parameters

coord: The coordinates of atoms
box: The simulation region. PBC is assumed
atype: The atom types
eval_fv: Evaluate force and virial

Returns

tot_e: The energy modification
tot_f: The force modification
tot_v: The virial modification

load_prefix: str

modify_data(data: dict) → None[source]

Modify data.

Parameters

data: Internal data of DeepmdData. Be a dict, has the following keys - coord coordinates - box simulation box - type atom types - find_energy tells if data has energy - find_force tells if data has force - find_virial tells if data has virial - energy energy - force force - virial virial

class deepmd.infer.EwaldRecp(hh, beta)[source]

Bases: object

Evaluate the reciprocal part of the Ewald sum

Methods

eval(coord, charge, box)

Evaluate

eval(coord: numpy.ndarray, charge: numpy.ndarray, box: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Evaluate

Parameters

coord: The coordinates of atoms
charge: The atomic charge
box: The simulation region. PBC is assumed

Returns

e: The energy
f: The force
v: The virial

deepmd.infer.calc_model_devi(coord, box, atype, models, fname=None, frequency=1)[source]

Python interface to calculate model deviation

Parameters

coordnumpy.ndarray, n_frames x n_atoms x 3: Coordinates of system to calculate
boxnumpy.ndarray or None, n_frames x 3 x 3: Box to specify periodic boundary condition. If None, no pbc will be used
atypenumpy.ndarray, n_atoms x 1: Atom types
modelslist of DeepPot models: Models used to evaluate deviation
fnamestr or None: File to dump results, default None
frequencyint: Steps between frames (if the system is given by molecular dynamics engine), default 1

Returns

model_devinumpy.ndarray, n_frames x 7: Model deviation results. The first column is index of steps, the other 6 columns are max_devi_v, min_devi_v, avg_devi_v, max_devi_f, min_devi_f, avg_devi_f.

Examples

>>> from deepmd.infer import calc_model_devi
>>> from deepmd.infer import DeepPot as DP
>>> import numpy as np
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> graphs = [DP("graph.000.pb"), DP("graph.001.pb")]
>>> model_devi = calc_model_devi(coord, cell, atype, graphs)

Submodules

deepmd.infer.data_modifier module

class deepmd.infer.data_modifier.DipoleChargeModifier(model_name: str, model_charge_map: List[float], sys_charge_map: List[float], ewald_h: float = 1, ewald_beta: float = 1)[source]

Bases: deepmd.infer.deep_dipole.DeepDipole

Parameters

model_name: The model file for the DeepDipole model
model_charge_map: Gives the amount of charge for the wfcc
sys_charge_map: Gives the amount of charge for the real atoms
ewald_h: Grid spacing of the reciprocal part of Ewald sum. Unit: A
ewald_beta: Splitting parameter of the Ewald sum. Unit: A^{-1}

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`build_fv_graph`()	Build the computational graph for the force and virial inference.
`eval`(coord, box, atype[, eval_fv])	Evaluate the modification
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`modify_data`(data)	Modify data.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

build_fv_graph() → tensorflow.python.framework.ops.Tensor[source]: Build the computational graph for the force and virial inference.

eval(coord: numpy.ndarray, box: numpy.ndarray, atype: numpy.ndarray, eval_fv: bool = True) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Evaluate the modification

Parameters

coord: The coordinates of atoms
box: The simulation region. PBC is assumed
atype: The atom types
eval_fv: Evaluate force and virial

Returns

tot_e: The energy modification
tot_f: The force modification
tot_v: The virial modification

load_prefix: str

modify_data(data: dict) → None[source]

Modify data.

Parameters

data: Internal data of DeepmdData. Be a dict, has the following keys - coord coordinates - box simulation box - type atom types - find_energy tells if data has energy - find_force tells if data has force - find_virial tells if data has virial - energy energy - force force - virial virial

deepmd.infer.deep_dipole module

class deepmd.infer.deep_dipole.DeepDipole(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

deepmd.infer.deep_eval module

class deepmd.infer.deep_eval.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = False)[source]

Bases: object

Common methods for DeepPot, DeepWFC, DeepPolar, …

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: False: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

load_prefix: str

make_natoms_vec(atom_types: numpy.ndarray) → numpy.ndarray[source]

Make the natom vector used by deepmd-kit.

Parameters

atom_types: The type of atoms

Returns

natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

property model_type: str

Get type of model.

:type:str

property model_version: str

Get version of model.

Returns

str: version of model

static reverse_map(vec: numpy.ndarray, imap: List[int]) → numpy.ndarray[source]

Reverse mapping of a vector according to the index map

Parameters

vec: Input vector. Be of shape [nframes, natoms, -1]
imap: Index map. Be of shape [natoms]

Returns

vec_out: Reverse mapped vector.

property sess: tensorflow.python.client.session.Session: Get TF session.

static sort_input(coord: numpy.ndarray, atom_type: numpy.ndarray, sel_atoms: Optional[List[int]] = None)[source]

Sort atoms in the system according their types.

Parameters

coord: The coordinates of atoms. Should be of shape [nframes, natoms, 3]
atom_type: The type of atoms Should be of shape [natoms]
sel_atom: The selected atoms by type

Returns

coord_out: The coordinates after sorting
atom_type_out: The atom types after sorting
idx_map: The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]
sel_atom_type: Only output if sel_atoms is not None The sorted selected atom types
sel_idx_map: Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.

deepmd.infer.deep_polar module

class deepmd.infer.deep_polar.DeepGlobalPolar(model_file: str, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filestr: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) → numpy.ndarray[source]

Evaluate the model.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
atomic: Not used in this model
fparam: Not used in this model
aparam: Not used in this model
efield: Not used in this model

Returns

tensor: The returned tensor If atomic == False then of size nframes x variable_dof else of size nframes x natoms x variable_dof

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.deep_polar.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

deepmd.infer.deep_pot module

class deepmd.infer.deep_pot.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = True)[source]

Bases: deepmd.infer.deep_eval.DeepEval

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Examples

>>> from deepmd.infer import DeepPot
>>> import numpy as np
>>> dp = DeepPot('graph.pb')
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> e, f, v = dp.eval(coord, cell, atype)

where e, f and v are predicted energy, force and virial of the system, respectively.

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the energy, force and virial by using this DP.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`get_dim_aparam`()	Get the number (dimension) of atomic parameters of this DP.
`get_dim_fparam`()	Get the number (dimension) of frame parameters of this DP.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Unsupported in this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) → Tuple[numpy.ndarray, ...][source]

Evaluate the energy, force and virial by using this DP.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
atomic: Calculate the atomic energy and virial
fparam: The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam: The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
efield: The external field on atoms. The array should be of size nframes x natoms x 3

Returns

energy: The system energy.
force: The force on each atom
virial: The virial
atom_energy: The atomic energy. Only returned when atomic == True
atom_virial: The atomic virial. Only returned when atomic == True

eval_descriptor(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) → numpy.array[source]

Evaluate descriptors by using this DP.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
fparam: The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam: The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
efield: The external field on atoms. The array should be of size nframes x natoms x 3

Returns

descriptor: Descriptors.

get_dim_aparam() → int[source]: Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int[source]: Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int[source]: Get the number of atom types of this model.

get_rcut() → float[source]: Get the cut-off radius of this model.

get_sel_type() → List[int][source]: Unsupported in this model.

get_type_map() → List[int][source]: Get the type map (element name of the atom types) of this model.

load_prefix: str

deepmd.infer.deep_tensor module

class deepmd.infer.deep_tensor.DeepTensor(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_eval.DeepEval

Evaluates a tensor model.

Parameters

model_file: str: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Get the number (dimension) of atomic parameters of this DP.
`get_dim_fparam`()	Get the number (dimension) of frame parameters of this DP.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = True, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) → numpy.ndarray[source]

Evaluate the model.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
atomic: If True (default), return the atomic tensor Otherwise return the global tensor
fparam: Not used in this model
aparam: Not used in this model
efield: Not used in this model

Returns

tensor: The returned tensor If atomic == False then of size nframes x output_dim else of size nframes x natoms x output_dim

eval_full(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.array] = None, aparam: Optional[numpy.array] = None, efield: Optional[numpy.array] = None) → Tuple[numpy.ndarray, ...][source]

Evaluate the model with interface similar to the energy model. Will return global tensor, component-wise force and virial and optionally atomic tensor and atomic virial.

Parameters

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_types: The atom types The list should contain natoms ints
atomic: Whether to calculate atomic tensor and virial
fparam: Not used in this model
aparam: Not used in this model
efield: Not used in this model

Returns

tensor: The global tensor. shape: [nframes x nout]
force: The component-wise force (negative derivative) on each atom. shape: [nframes x nout x natoms x 3]
virial: The component-wise virial of the tensor. shape: [nframes x nout x 9]
atom_tensor: The atomic tensor. Only returned when atomic == True shape: [nframes x natoms x nout]
atom_virial: The atomic virial. Only returned when atomic == True shape: [nframes x nout x natoms x 9]

get_dim_aparam() → int[source]: Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int[source]: Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int[source]: Get the number of atom types of this model.

get_rcut() → float[source]: Get the cut-off radius of this model.

get_sel_type() → List[int][source]: Get the selected atom types of this model.

get_type_map() → List[int][source]: Get the type map (element name of the atom types) of this model.

load_prefix: str

tensors = {'t_box': 't_box:0', 't_coord': 't_coord:0', 't_mesh': 't_mesh:0', 't_natoms': 't_natoms:0', 't_ntypes': 'descrpt_attr/ntypes:0', 't_ouput_dim': 'model_attr/output_dim:0', 't_rcut': 'descrpt_attr/rcut:0', 't_sel_type': 'model_attr/sel_type:0', 't_tmap': 'model_attr/tmap:0', 't_type': 't_type:0'}

deepmd.infer.deep_wfc module

class deepmd.infer.deep_wfc.DeepWFC(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: deepmd.infer.deep_tensor.DeepTensor

Constructor.

Parameters

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_full`(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
`get_dim_aparam`()	Unsupported in this model.
`get_dim_fparam`()	Unsupported in this model.
`get_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Get the selected atom types of this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types)	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map
`sort_input`(coord, atom_type[, sel_atoms])	Sort atoms in the system according their types.

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

deepmd.infer.ewald_recp module

class deepmd.infer.ewald_recp.EwaldRecp(hh, beta)[source]

Bases: object

Evaluate the reciprocal part of the Ewald sum

Methods

eval(coord, charge, box)

Evaluate

eval(coord: numpy.ndarray, charge: numpy.ndarray, box: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Evaluate

Parameters

coord: The coordinates of atoms
charge: The atomic charge
box: The simulation region. PBC is assumed

Returns

e: The energy
f: The force
v: The virial

deepmd.infer.model_devi module

deepmd.infer.model_devi.calc_model_devi(coord, box, atype, models, fname=None, frequency=1)[source]

Python interface to calculate model deviation

Parameters

coordnumpy.ndarray, n_frames x n_atoms x 3: Coordinates of system to calculate
boxnumpy.ndarray or None, n_frames x 3 x 3: Box to specify periodic boundary condition. If None, no pbc will be used
atypenumpy.ndarray, n_atoms x 1: Atom types
modelslist of DeepPot models: Models used to evaluate deviation
fnamestr or None: File to dump results, default None
frequencyint: Steps between frames (if the system is given by molecular dynamics engine), default 1

Returns

model_devinumpy.ndarray, n_frames x 7: Model deviation results. The first column is index of steps, the other 6 columns are max_devi_v, min_devi_v, avg_devi_v, max_devi_f, min_devi_f, avg_devi_f.

Examples

>>> from deepmd.infer import calc_model_devi
>>> from deepmd.infer import DeepPot as DP
>>> import numpy as np
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> graphs = [DP("graph.000.pb"), DP("graph.001.pb")]
>>> model_devi = calc_model_devi(coord, cell, atype, graphs)

deepmd.infer.model_devi.calc_model_devi_e(es: numpy.ndarray)[source]

Parameters

esnumpy.ndarray: size of `n_models x n_frames x n_atoms

deepmd.infer.model_devi.calc_model_devi_f(fs: numpy.ndarray)[source]

Parameters

fsnumpy.ndarray: size of n_models x n_frames x n_atoms x 3

deepmd.infer.model_devi.calc_model_devi_v(vs: numpy.ndarray)[source]

Parameters

vsnumpy.ndarray: size of n_models x n_frames x 9

deepmd.infer.model_devi.make_model_devi(*, models: list, system: str, set_prefix: str, output: str, frequency: int, **kwargs)[source]

Make model deviation calculation

Parameters

models: list: A list of paths of models to use for making model deviation
system: str: The path of system to make model deviation calculation
set_prefix: str: The set prefix of the system
output: str: The output file for model deviation results
frequency: int: The number of steps that elapse between writing coordinates in a trajectory by a MD engine (such as Gromacs / Lammps). This paramter is used to determine the index in the output file.

deepmd.infer.model_devi.write_model_devi_out(devi: numpy.ndarray, fname: str, header: str = '')[source]

Parameters

devinumpy.ndarray: the first column is the steps index
fnamestr: the file name to dump
headerstr, default=””: the header to dump

deepmd.loggers package

Module taking care of logging duties.

deepmd.loggers.set_log_handles(level: int, log_path: Optional[Path] = None, mpi_log: Optional[str] = None)[source]

Set desired level for package loggers and add file handlers.

Parameters

level: int: logging level
log_path: Optional[str]: path to log file, if None logs will be send only to console. If the parent directory does not exist it will be automatically created, by default None
mpi_logOptional[str], optional: mpi log type. Has three options. master will output logs to file and console only from rank==0. collect will write messages from all ranks to one file opened under rank==0 and to console. workers will open one log file for each worker designated by its rank, console behaviour is the same as for collect. If this argument is specified, package ‘mpi4py’ must be already installed. by default None

Raises

RuntimeError: If the argument mpi_log is specified, package mpi4py is not installed.

Notes

Logging levels:

	our notation	python logging	tensorflow cpp	OpenMP
debug	10	10	0	1/on/true/yes
info	20	20	1	0/off/false/no
warning	30	30	2	0/off/false/no
error	40	40	3	0/off/false/no

References

https://groups.google.com/g/mpi4py/c/SaNzc8bdj6U https://stackoverflow.com/questions/35869137/avoid-tensorflow-print-on-standard-error https://stackoverflow.com/questions/56085015/suppress-openmp-debug-messages-when-running-tensorflow-on-cpu

Submodules

deepmd.loggers.loggers module

Logger initialization for package.

deepmd.loggers.loggers.set_log_handles(level: int, log_path: Optional[Path] = None, mpi_log: Optional[str] = None)[source]

Set desired level for package loggers and add file handlers.

Parameters

level: int: logging level
log_path: Optional[str]: path to log file, if None logs will be send only to console. If the parent directory does not exist it will be automatically created, by default None
mpi_logOptional[str], optional: mpi log type. Has three options. master will output logs to file and console only from rank==0. collect will write messages from all ranks to one file opened under rank==0 and to console. workers will open one log file for each worker designated by its rank, console behaviour is the same as for collect. If this argument is specified, package ‘mpi4py’ must be already installed. by default None

Raises

RuntimeError: If the argument mpi_log is specified, package mpi4py is not installed.

Notes

Logging levels:

	our notation	python logging	tensorflow cpp	OpenMP
debug	10	10	0	1/on/true/yes
info	20	20	1	0/off/false/no
warning	30	30	2	0/off/false/no
error	40	40	3	0/off/false/no

References

https://groups.google.com/g/mpi4py/c/SaNzc8bdj6U https://stackoverflow.com/questions/35869137/avoid-tensorflow-print-on-standard-error https://stackoverflow.com/questions/56085015/suppress-openmp-debug-messages-when-running-tensorflow-on-cpu

deepmd.loss package

Submodules

deepmd.loss.ener module

class deepmd.loss.ener.EnerDipoleLoss(starter_learning_rate: float, start_pref_e: float = 0.1, limit_pref_e: float = 1.0, start_pref_ed: float = 1.0, limit_pref_ed: float = 1.0)[source]

Bases: deepmd.loss.loss.Loss

Methods

`build`(learning_rate, natoms, model_dict, ...)	Build the loss function graph.
`eval`(sess, feed_dict, natoms)	Eval the loss function.

print_header
print_on_training

build(learning_rate, natoms, model_dict, label_dict, suffix)[source]

Build the loss function graph.

Parameters

learning_ratetf.Tensor: learning rate
natomstf.Tensor: number of atoms
model_dictdict[str, tf.Tensor]: A dictionary that maps model keys to tensors
label_dictdict[str, tf.Tensor]: A dictionary that maps label keys to tensors
suffixstr: suffix

Returns

tf.Tensor: the total squared loss
dict[str, tf.Tensor]: A dictionary that maps loss keys to more loss tensors

eval(sess, feed_dict, natoms)[source]

Eval the loss function.

Parameters

sesstf.Session: TensorFlow session
feed_dictdict[tf.placeholder, tf.Tensor]: A dictionary that maps graph elements to values
natomstf.Tensor: number of atoms

Returns

dict: A dictionary that maps keys to values. It should contain key natoms

static print_header()[source]

print_on_training(tb_writer, cur_batch, sess, natoms, feed_dict_test, feed_dict_batch)[source]

class deepmd.loss.ener.EnerStdLoss(starter_learning_rate: float, start_pref_e: float = 0.02, limit_pref_e: float = 1.0, start_pref_f: float = 1000, limit_pref_f: float = 1.0, start_pref_v: float = 0.0, limit_pref_v: float = 0.0, start_pref_ae: float = 0.0, limit_pref_ae: float = 0.0, start_pref_pf: float = 0.0, limit_pref_pf: float = 0.0, relative_f: Optional[float] = None, enable_atom_ener_coeff: bool = False)[source]

Bases: deepmd.loss.loss.Loss

Standard loss function for DP models

Parameters

enable_atom_ener_coeffbool: if true, the energy will be computed as sum_i c_i E_i

Methods

`build`(learning_rate, natoms, model_dict, ...)	Build the loss function graph.
`eval`(sess, feed_dict, natoms)	Eval the loss function.

print_header
print_on_training

build(learning_rate, natoms, model_dict, label_dict, suffix)[source]

Build the loss function graph.

Parameters

learning_ratetf.Tensor: learning rate
natomstf.Tensor: number of atoms
model_dictdict[str, tf.Tensor]: A dictionary that maps model keys to tensors
label_dictdict[str, tf.Tensor]: A dictionary that maps label keys to tensors
suffixstr: suffix

Returns

tf.Tensor: the total squared loss
dict[str, tf.Tensor]: A dictionary that maps loss keys to more loss tensors

eval(sess, feed_dict, natoms)[source]

Eval the loss function.

Parameters

sesstf.Session: TensorFlow session
feed_dictdict[tf.placeholder, tf.Tensor]: A dictionary that maps graph elements to values
natomstf.Tensor: number of atoms

Returns

dict: A dictionary that maps keys to values. It should contain key natoms

print_header()[source]

print_on_training(tb_writer, cur_batch, sess, natoms, feed_dict_test, feed_dict_batch)[source]

deepmd.loss.loss module

class deepmd.loss.loss.Loss[source]

Bases: object

The abstract class for the loss function.

Methods

`build`(learning_rate, natoms, model_dict, ...)	Build the loss function graph.
`eval`(sess, feed_dict, natoms)	Eval the loss function.

abstract build(learning_rate: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, model_dict: Dict[str, tensorflow.python.framework.ops.Tensor], label_dict: Dict[str, tensorflow.python.framework.ops.Tensor], suffix: str) → Tuple[tensorflow.python.framework.ops.Tensor, Dict[str, tensorflow.python.framework.ops.Tensor]][source]

Build the loss function graph.

Parameters

learning_ratetf.Tensor: learning rate
natomstf.Tensor: number of atoms
model_dictdict[str, tf.Tensor]: A dictionary that maps model keys to tensors
label_dictdict[str, tf.Tensor]: A dictionary that maps label keys to tensors
suffixstr: suffix

Returns

tf.Tensor: the total squared loss
dict[str, tf.Tensor]: A dictionary that maps loss keys to more loss tensors

abstract eval(sess: tensorflow.python.client.session.Session, feed_dict: Dict[tensorflow.python.ops.array_ops.placeholder, tensorflow.python.framework.ops.Tensor], natoms: tensorflow.python.framework.ops.Tensor) → dict[source]

Eval the loss function.

Parameters

sesstf.Session: TensorFlow session
feed_dictdict[tf.placeholder, tf.Tensor]: A dictionary that maps graph elements to values
natomstf.Tensor: number of atoms

Returns

dict: A dictionary that maps keys to values. It should contain key natoms

deepmd.loss.tensor module

class deepmd.loss.tensor.TensorLoss(jdata, **kwarg)[source]

Bases: deepmd.loss.loss.Loss

Loss function for tensorial properties.

Methods

`build`(learning_rate, natoms, model_dict, ...)	Build the loss function graph.
`eval`(sess, feed_dict, natoms)	Eval the loss function.

print_header
print_on_training

build(learning_rate, natoms, model_dict, label_dict, suffix)[source]

Build the loss function graph.

Parameters

learning_ratetf.Tensor: learning rate
natomstf.Tensor: number of atoms
model_dictdict[str, tf.Tensor]: A dictionary that maps model keys to tensors
label_dictdict[str, tf.Tensor]: A dictionary that maps label keys to tensors
suffixstr: suffix

Returns

tf.Tensor: the total squared loss
dict[str, tf.Tensor]: A dictionary that maps loss keys to more loss tensors

eval(sess, feed_dict, natoms)[source]

Eval the loss function.

Parameters

sesstf.Session: TensorFlow session
feed_dictdict[tf.placeholder, tf.Tensor]: A dictionary that maps graph elements to values
natomstf.Tensor: number of atoms

Returns

dict: A dictionary that maps keys to values. It should contain key natoms

print_header()[source]

print_on_training(tb_writer, cur_batch, sess, natoms, feed_dict_test, feed_dict_batch)[source]

deepmd.model package

Submodules

deepmd.model.ener module

class deepmd.model.ener.EnerModel(descrpt, fitting, typeebd=None, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01, use_srtab: Optional[str] = None, smin_alpha: Optional[float] = None, sw_rmin: Optional[float] = None, sw_rmax: Optional[float] = None)[source]

Bases: deepmd.model.model.Model

Energy model.

Parameters

descrpt: Descriptor
fitting: Fitting net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
data_stat_nbatch: Number of frames used for data statistic
data_stat_protect: Protect parameter for atomic energy regression
use_srtab: The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.
smin_alpha: The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when use_srtab is provided.
sw_rmin: The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.
sw_rmin: The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

Methods

init_variables(graph, graph_def[, ...])

Init the embedding net variables with the given frozen model

build
data_stat
get_ntypes
get_rcut
get_type_map

build(coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, suffix='', reuse=None)[source]

data_stat(data)[source]

get_ntypes()[source]

get_rcut()[source]

get_type_map()[source]

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

model_type = 'ener'

deepmd.model.model module

class deepmd.model.model.Model[source]

Bases: object

Methods

init_variables(graph, graph_def[, ...])

Init the embedding net variables with the given frozen model

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

deepmd.model.model_stat module

deepmd.model.model_stat.make_stat_input(data, nbatches, merge_sys=True)[source]

pack data for statistics

Parameters

data:: The data
merge_sys: bool (True): Merge system data

Returns

all_stat:

A dictionary of list of list storing data for stat. if merge_sys == False data can be accessed by

all_stat[key][sys_idx][batch_idx][frame_idx]

else merge_sys == True can be accessed by: all_stat[key][batch_idx][frame_idx]

deepmd.model.model_stat.merge_sys_stat(all_stat)[source]

deepmd.model.tensor module

class deepmd.model.tensor.DipoleModel(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)[source]

Bases: deepmd.model.tensor.TensorModel

Methods

init_variables(graph, graph_def[, ...])

Init the embedding net variables with the given frozen model

build
data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.tensor.GlobalPolarModel(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)[source]

Bases: deepmd.model.tensor.TensorModel

Methods

init_variables(graph, graph_def[, ...])

Init the embedding net variables with the given frozen model

build
data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.tensor.PolarModel(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)[source]

Bases: deepmd.model.tensor.TensorModel

Methods

init_variables(graph, graph_def[, ...])

Init the embedding net variables with the given frozen model

build
data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.tensor.TensorModel(tensor_name: str, descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)[source]

Bases: deepmd.model.model.Model

Tensor model.

Parameters

tensor_name: Name of the tensor.
descrpt: Descriptor
fitting: Fitting net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
data_stat_nbatch: Number of frames used for data statistic
data_stat_protect: Protect parameter for atomic energy regression

Methods

init_variables(graph, graph_def[, ...])

Init the embedding net variables with the given frozen model

build
data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

build(coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, suffix='', reuse=None)[source]

data_stat(data)[source]

get_ntypes()[source]

get_out_size()[source]

get_rcut()[source]

get_sel_type()[source]

get_type_map()[source]

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

class deepmd.model.tensor.WFCModel(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)[source]

Bases: deepmd.model.tensor.TensorModel

Methods

init_variables(graph, graph_def[, ...])

Init the embedding net variables with the given frozen model

build
data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

deepmd.nvnmd package

Subpackages

deepmd.nvnmd.data package

nvnmd.data

Provides

hardware configuration
default input script
title and citation

Data

jdata_sys

action configuration

jdata_config

hardware configuration

dscp: descriptor configuration
fitn: fitting network configuration
size: ram capacity
ctrl: control flag, such as Time Division Multiplexing (TDM)
nbit: number of bits of fixed-point number

jdata_config_16 (disable)

difference with configure fitting size as 16

jdata_config_32 (disable)

difference with configure fitting size as 32

jdata_config_64 (disable)

difference with configure fitting size as 64

jdata_config_128 (default)

difference with configure fitting size as 128

jdata_configs

all configure of jdata_config{nfit_node}

jdata_deepmd_input

default input script for nvnmd training

NVNMD_WELCOME

nvnmd title when logging

NVNMD_CITATION

citation of nvnmd

Submodules

deepmd.nvnmd.data.data module

deepmd.nvnmd.descriptor package

nvnmd.se_a

Provides

building descriptor with continuous embedding network
building descriptor with quantized embedding network

Submodules

deepmd.nvnmd.descriptor.se_a module

deepmd.nvnmd.descriptor.se_a.build_davg_dstd()[source]: Get the davg and dstd from the dictionary nvnmd_cfg. The davg and dstd have been obtained by training CNN

deepmd.nvnmd.descriptor.se_a.build_op_descriptor()[source]: Replace se_a.py/DescrptSeA/build

deepmd.nvnmd.descriptor.se_a.descrpt2r4(inputs, natoms)[source]: Replace $r_{ji} \rightarrow r'_{ji}$ where $r_{ji} = (x_{ji}, y_{ji}, z_{ji})$ and $r'_{ji} = (s_{ji}, \frac{s_{ji} x_{ji}}{r_{ji}}, \frac{s_{ji} y_{ji}}{r_{ji}}, \frac{s_{ji} z_{ji}}{r_{ji}})$

deepmd.nvnmd.descriptor.se_a.filter_GR2D(xyz_scatter_1)[source]: Replace se_a.py/_filter

deepmd.nvnmd.descriptor.se_a.filter_lower_R42GR(type_i, type_input, inputs_i, is_exclude, activation_fn, bavg, stddev, trainable, suffix, seed, seed_shift, uniform_seed, filter_neuron, filter_precision, filter_resnet_dt, embedding_net_variables)[source]: Replace se_a.py/DescrptSeA/_filter_lower

deepmd.nvnmd.entrypoints package

class deepmd.nvnmd.entrypoints.MapTable(config_file: str, weight_file: str, map_file: str)[source]

Bases: object

Generate the mapping table describing the relastionship of atomic distance, cutoff function, and embedding matrix.

three mapping table will be built:

\(r^2_{ji} \rightarrow s_{ji}\)
\(r^2_{ji} \rightarrow sr_{ji}\)
\(r^2_{ji} \rightarrow \mathcal{G}_{ji}\)

where $s_{ji}$ is cut-off function, $sr_{ji} = \frac{s(r_{ji})}{r_{ji}}$, and $\mathcal{G}_{ji}$ is embedding matrix.

The mapping funciton can be define as:

\(y = f(x) = y_{k} + (x - x_{k}) * dy_{k}\)
\(y_{k} = f(x_{k})\)
\(dy_{k} = \frac{f(x_{k+1}) - f(x_{k})}{dx}\)
\(x_{k} \leq x < x_{k+1}\)
\(x_{k} = k * dx\)

where $dx$ is interpolation interval.

Parameters

config_file: input file name an .npy file containing the configuration information of NVNMD model
weight_file: input file name an .npy file containing the weights of NVNMD model
map_file: output file name an .npy file containing the mapping tables of NVNMD model

References

DOI: 10.1038/s41524-022-00773-z

Methods

build_dG_ds
build_ds_dr
build_map
build_r2s
build_r2s_r2ds
build_s2G
build_s2G_s2dG
qqq
run_s2G
run_u2s

build_dG_ds(G, s)[source]

build_ds_dr(r2, s, sr)[source]

build_map()[source]

build_r2s(r2)[source]

build_r2s_r2ds()[source]

build_s2G(s)[source]

build_s2G_s2dG()[source]

qqq(dat, NBIT_FEA_FL, NBIT_FEA_X, is_set_zero=False)[source]

run_s2G(dat)[source]

run_u2s()[source]

class deepmd.nvnmd.entrypoints.Wrap(config_file: str, weight_file: str, map_file: str, model_file: str)[source]

Bases: object

Generate the binary model file (model.pb) the model file can be use to run the NVNMD with lammps the pair style need set as:

pair_style nvnmd model.pb
pair_coeff * *

Parameters

config_file: input file name an .npy file containing the configuration information of NVNMD model
weight_file: input file name an .npy file containing the weights of NVNMD model
map_file: input file name an .npy file containing the mapping tables of NVNMD model
model_file: output file name an .pb file containing the model using in the NVNMD

References

DOI: 10.1038/s41524-022-00773-z

Methods

`wrap_dscp`()	Wrap the configuration of descriptor
`wrap_fitn`()	Wrap the weights of fitting net
`wrap_map`()	Wrap the mapping table of embedding network

wrap
wrap_bias
wrap_head
wrap_weight

wrap()[source]

wrap_bias(bias, NBIT_SUM, NBIT_DATA_FL)[source]

wrap_dscp()[source]: Wrap the configuration of descriptor

wrap_fitn()[source]: Wrap the weights of fitting net

wrap_head(NCFG, NNET, NFEA)[source]

wrap_map()[source]: Wrap the mapping table of embedding network

wrap_weight(weight, NBIT_WEIGHT, NBIT_WEIGHT_FL)[source]

deepmd.nvnmd.entrypoints.save_weight(sess, file_name: str = 'nvnmd/weight.npy')[source]: Save the dictionary of weight to a npy file

Submodules

deepmd.nvnmd.entrypoints.freeze module

deepmd.nvnmd.entrypoints.freeze.filter_tensorVariableList(tensorVariableList) → dict[source]: Get the name of variable for NVNMD

descrpt_attr/t_avg:0

descrpt_attr/t_std:0

filter_type_{atom i}/matrix_{layer l}_{atomj}:0

filter_type_{atom i}/bias_{layer l}_{atomj}:0

layer_{layer l}_type_{atom i}/matrix:0

layer_{layer l}_type_{atom i}/bias:0

final_layer_type_{atom i}/matrix:0

final_layer_type_{atom i}/bias:0

deepmd.nvnmd.entrypoints.freeze.save_weight(sess, file_name: str = 'nvnmd/weight.npy')[source]: Save the dictionary of weight to a npy file

deepmd.nvnmd.entrypoints.mapt module

class deepmd.nvnmd.entrypoints.mapt.MapTable(config_file: str, weight_file: str, map_file: str)[source]

Bases: object

Generate the mapping table describing the relastionship of atomic distance, cutoff function, and embedding matrix.

three mapping table will be built:

\(r^2_{ji} \rightarrow s_{ji}\)
\(r^2_{ji} \rightarrow sr_{ji}\)
\(r^2_{ji} \rightarrow \mathcal{G}_{ji}\)

where $s_{ji}$ is cut-off function, $sr_{ji} = \frac{s(r_{ji})}{r_{ji}}$, and $\mathcal{G}_{ji}$ is embedding matrix.

The mapping funciton can be define as:

\(y = f(x) = y_{k} + (x - x_{k}) * dy_{k}\)
\(y_{k} = f(x_{k})\)
\(dy_{k} = \frac{f(x_{k+1}) - f(x_{k})}{dx}\)
\(x_{k} \leq x < x_{k+1}\)
\(x_{k} = k * dx\)

where $dx$ is interpolation interval.

Parameters

config_file: input file name an .npy file containing the configuration information of NVNMD model
weight_file: input file name an .npy file containing the weights of NVNMD model
map_file: output file name an .npy file containing the mapping tables of NVNMD model

References

DOI: 10.1038/s41524-022-00773-z

Methods

build_dG_ds
build_ds_dr
build_map
build_r2s
build_r2s_r2ds
build_s2G
build_s2G_s2dG
qqq
run_s2G
run_u2s

build_dG_ds(G, s)[source]

build_ds_dr(r2, s, sr)[source]

build_map()[source]

build_r2s(r2)[source]

build_r2s_r2ds()[source]

build_s2G(s)[source]

build_s2G_s2dG()[source]

qqq(dat, NBIT_FEA_FL, NBIT_FEA_X, is_set_zero=False)[source]

run_s2G(dat)[source]

run_u2s()[source]

deepmd.nvnmd.entrypoints.mapt.mapt(*, nvnmd_config: Optional[str] = 'nvnmd/config.npy', nvnmd_weight: Optional[str] = 'nvnmd/weight.npy', nvnmd_map: Optional[str] = 'nvnmd/map.npy', **kwargs)[source]

deepmd.nvnmd.entrypoints.train module

deepmd.nvnmd.entrypoints.train.add_path(p, p2)[source]

deepmd.nvnmd.entrypoints.train.normalized_input(fn, PATH_CNN)[source]: Normalize a input script file for continuous neural network

deepmd.nvnmd.entrypoints.train.normalized_input_qnn(jdata, PATH_QNN, CONFIG_CNN, WEIGHT_CNN, MAP_CNN)[source]: Normalize a input script file for quantize neural network

deepmd.nvnmd.entrypoints.train.replace_path(p, p2)[source]

deepmd.nvnmd.entrypoints.train.train_nvnmd(*, INPUT: str, step: str, **kwargs)[source]

deepmd.nvnmd.entrypoints.wrap module

class deepmd.nvnmd.entrypoints.wrap.Wrap(config_file: str, weight_file: str, map_file: str, model_file: str)[source]

Bases: object

Generate the binary model file (model.pb) the model file can be use to run the NVNMD with lammps the pair style need set as:

pair_style nvnmd model.pb
pair_coeff * *

Parameters

config_file: input file name an .npy file containing the configuration information of NVNMD model
weight_file: input file name an .npy file containing the weights of NVNMD model
map_file: input file name an .npy file containing the mapping tables of NVNMD model
model_file: output file name an .pb file containing the model using in the NVNMD

References

DOI: 10.1038/s41524-022-00773-z

Methods

`wrap_dscp`()	Wrap the configuration of descriptor
`wrap_fitn`()	Wrap the weights of fitting net
`wrap_map`()	Wrap the mapping table of embedding network

wrap
wrap_bias
wrap_head
wrap_weight

wrap()[source]

wrap_bias(bias, NBIT_SUM, NBIT_DATA_FL)[source]

wrap_dscp()[source]: Wrap the configuration of descriptor

wrap_fitn()[source]: Wrap the weights of fitting net

wrap_head(NCFG, NNET, NFEA)[source]

wrap_map()[source]: Wrap the mapping table of embedding network

wrap_weight(weight, NBIT_WEIGHT, NBIT_WEIGHT_FL)[source]

deepmd.nvnmd.entrypoints.wrap.wrap(*, nvnmd_config: Optional[str] = 'nvnmd/config.npy', nvnmd_weight: Optional[str] = 'nvnmd/weight.npy', nvnmd_map: Optional[str] = 'nvnmd/map.npy', nvnmd_model: Optional[str] = 'nvnmd/model.pb', **kwargs)[source]

deepmd.nvnmd.fit package

nvnmd.fit

Provides

continuous fitting network
quantized fitting network

Submodules

deepmd.nvnmd.fit.ener module

deepmd.nvnmd.utils package

class deepmd.nvnmd.utils.Encode[source]

Bases: object

Encoding value as hex, bin, and dec format

Methods

`bin2hex`(data)	Convert binary string list to hex string list
`bin2hex_str`(sbin)	Convert binary string to hex string
`check_dec`(idec, nbit[, signed, name])	Check whether the data (idec) is in the range range is $[0, 2^nbit-1]$ for unsigned range is $[-2^{nbit-1}, 2^{nbit-1}-1]$ for signed
`dec2bin`(idec[, nbit, signed, name])	Convert dec array to binary string list
`extend_bin`(slbin, nfull)	Extend the element of list (slbin) to the length (nfull)
`extend_hex`(slhex, nfull)	Extend the element of list (slhex) to the length (nfull)
`extend_list`(slbin, nfull)	Extend the list (slbin) to the length (nfull) the attched element of list is 0
`hex2bin`(data)	Convert hex string list to binary string list
`hex2bin_str`(shex)	Convert hex string to binary string
`merge_bin`(slbin, nmerge)	Merge binary string list per nmerge value
`qc`(v[, nbit])	Quantize value using ceil
`qf`(v[, nbit])	Quantize value using floor
`qr`(v[, nbit])	Quantize value using round
`reverse_bin`(slbin, nreverse)	Reverse binary string list per nreverse value
`split_bin`(sbin, nbit)	Split sbin into many segment with the length nbit

bin2hex(data)[source]: Convert binary string list to hex string list

bin2hex_str(sbin)[source]: Convert binary string to hex string

check_dec(idec, nbit, signed=False, name='')[source]: Check whether the data (idec) is in the range range is $[0, 2^nbit-1]$ for unsigned range is $[-2^{nbit-1}, 2^{nbit-1}-1]$ for signed

dec2bin(idec, nbit=10, signed=False, name='')[source]: Convert dec array to binary string list

extend_bin(slbin, nfull)[source]

Extend the element of list (slbin) to the length (nfull)

such as, when

slbin = [‘10010’,’10100’],

nfull = 6

extent to

[‘010010’,’010100’]

extend_hex(slhex, nfull)[source]: Extend the element of list (slhex) to the length (nfull)

extend_list(slbin, nfull)[source]

Extend the list (slbin) to the length (nfull) the attched element of list is 0

such as, when

slbin = [‘10010’,’10100’],

nfull = 4

extent it to

[‘10010’,’10100’,’00000’,’00000]

hex2bin(data)[source]: Convert hex string list to binary string list

hex2bin_str(shex)[source]: Convert hex string to binary string

merge_bin(slbin, nmerge)[source]: Merge binary string list per nmerge value

qc(v, nbit: int = 14)[source]: Quantize value using ceil

qf(v, nbit: int = 14)[source]: Quantize value using floor

qr(v, nbit: int = 14)[source]: Quantize value using round

reverse_bin(slbin, nreverse)[source]: Reverse binary string list per nreverse value

split_bin(sbin, nbit: int)[source]: Split sbin into many segment with the length nbit

class deepmd.nvnmd.utils.FioBin[source]

Bases: object

Input and output for binary file

Methods

`load`([file_name, default_value])	Load binary file into bytes value
`save`([file_name, data])	Save hex string into binary file

load(file_name='', default_value='')[source]: Load binary file into bytes value

save(file_name: str = '', data: str = '')[source]: Save hex string into binary file

class deepmd.nvnmd.utils.FioDic[source]

Bases: object

Input and output for dict class data the file can be .json or .npy file containing a dictionary

Methods

update(jdata, jdata_o)

Update key-value pair is key in jdata_o.keys()

get
load
save

get(jdata, key, default_value)[source]

load(file_name='', default_value={})[source]

save(file_name='', dic={})[source]

update(jdata, jdata_o)[source]: Update key-value pair is key in jdata_o.keys()

class deepmd.nvnmd.utils.FioTxt[source]

Bases: object

Input and output for .txt file with string

Methods

`load`([file_name, default_value])	Load .txt file into string list
`save`([file_name, data])	Save string list into .txt file

load(file_name='', default_value=[])[source]: Load .txt file into string list

save(file_name: str = '', data: list = [])[source]: Save string list into .txt file

deepmd.nvnmd.utils.get_filter_weight(weights: dict, spe_i: int, spe_j: int, layer_l: int)[source]

Get weight and bias of embedding network

Parameters

spe_i(int): special order of central atom i 0~ntype-1
spe_j(int): special order of neighbor atom j 0~ntype-1
layer_l: layer order in embedding network 1~nlayer

deepmd.nvnmd.utils.get_fitnet_weight(weights: dict, spe_i: int, layer_l: int, nlayer: int = 10)[source]

Get weight and bias of fitting network

Parameters

spe_i(int): special order of central atom i 0~ntype-1
layer_l(int): layer order in embedding network 0~nlayer-1

deepmd.nvnmd.utils.map_nvnmd(x, map_y, map_dy, prec, nbit=None)[source]: Mapping function implemented by numpy

deepmd.nvnmd.utils.nvnmd_args()[source]

deepmd.nvnmd.utils.one_layer(inputs, outputs_size, activation_fn=<function tanh>, precision=tf.float64, stddev=1.0, bavg=0.0, name='linear', reuse=None, seed=None, use_timestep=False, trainable=True, useBN=False, uniform_seed=False, initial_variables=None, mixed_prec=None, final_layer=False)[source]: Build one layer with continuous or quantized value. Its weight and bias can be initialed with random or constant value.

Submodules

deepmd.nvnmd.utils.argcheck module

deepmd.nvnmd.utils.argcheck.nvnmd_args()[source]

deepmd.nvnmd.utils.config module

class deepmd.nvnmd.utils.config.NvnmdConfig(jdata: dict)[source]

Bases: object

Configuration for NVNMD record the message of model such as size, using nvnmd or not

Parameters

jdata: a dictionary of input script

References

DOI: 10.1038/s41524-022-00773-z

Methods

`disp_message`()	Display the log of NVNMD
`get_deepmd_jdata`()	Generate input script with member element one by one
`get_dscp_jdata`()	Generate model/descriptor in input script
`get_fitn_jdata`()	Generate model/fitting_net in input script
`get_learning_rate_jdata`()	Generate learning_rate in input script
`get_loss_jdata`()	Generate loss in input script
`get_model_jdata`()	Generate model in input script
`get_nvnmd_jdata`()	Generate nvnmd in input script
`get_training_jdata`()	Generate training in input script
`init_ctrl`(jdata[, jdata_parent])	Initial members about control signal
`init_dscp`(jdata[, jdata_parent])	Initial members about descriptor
`init_fitn`(jdata[, jdata_parent])	Initial members about fitting network
`init_from_config`(jdata)	Initial member element one by one
`init_from_deepmd_input`(jdata)	Initial members with input script of deepmd
`init_from_jdata`([jdata])	Initial this class with jdata loaded from input script
`init_nbit`(jdata[, jdata_parent])	Initial members about quantification precision
`init_net_size`()	Initial net_size
`init_size`(jdata[, jdata_parent])	Initial members about ram capacity
`init_train_mode`([mod])	Configure for taining cnn or qnn
`init_value`()	Initial member with dict
`save`([file_name])	Save all configuration to file

disp_message()[source]: Display the log of NVNMD

get_deepmd_jdata()[source]: Generate input script with member element one by one

get_dscp_jdata()[source]: Generate model/descriptor in input script

get_fitn_jdata()[source]: Generate model/fitting_net in input script

get_learning_rate_jdata()[source]: Generate learning_rate in input script

get_loss_jdata()[source]: Generate loss in input script

get_model_jdata()[source]: Generate model in input script

get_nvnmd_jdata()[source]: Generate nvnmd in input script

get_training_jdata()[source]: Generate training in input script

init_ctrl(jdata: dict, jdata_parent: dict = {}) → dict[source]: Initial members about control signal

init_dscp(jdata: dict, jdata_parent: dict = {}) → dict[source]: Initial members about descriptor

init_fitn(jdata: dict, jdata_parent: dict = {}) → dict[source]: Initial members about fitting network

init_from_config(jdata)[source]: Initial member element one by one

init_from_deepmd_input(jdata)[source]: Initial members with input script of deepmd

init_from_jdata(jdata: dict = {})[source]: Initial this class with jdata loaded from input script

init_nbit(jdata: dict, jdata_parent: dict = {}) → dict[source]: Initial members about quantification precision

init_net_size()[source]: Initial net_size

init_size(jdata: dict, jdata_parent: dict = {}) → dict[source]: Initial members about ram capacity

init_train_mode(mod='cnn')[source]: Configure for taining cnn or qnn

init_value()[source]: Initial member with dict

save(file_name=None)[source]: Save all configuration to file

deepmd.nvnmd.utils.encode module

class deepmd.nvnmd.utils.encode.Encode[source]

Bases: object

Encoding value as hex, bin, and dec format

Methods

`bin2hex`(data)	Convert binary string list to hex string list
`bin2hex_str`(sbin)	Convert binary string to hex string
`check_dec`(idec, nbit[, signed, name])	Check whether the data (idec) is in the range range is $[0, 2^nbit-1]$ for unsigned range is $[-2^{nbit-1}, 2^{nbit-1}-1]$ for signed
`dec2bin`(idec[, nbit, signed, name])	Convert dec array to binary string list
`extend_bin`(slbin, nfull)	Extend the element of list (slbin) to the length (nfull)
`extend_hex`(slhex, nfull)	Extend the element of list (slhex) to the length (nfull)
`extend_list`(slbin, nfull)	Extend the list (slbin) to the length (nfull) the attched element of list is 0
`hex2bin`(data)	Convert hex string list to binary string list
`hex2bin_str`(shex)	Convert hex string to binary string
`merge_bin`(slbin, nmerge)	Merge binary string list per nmerge value
`qc`(v[, nbit])	Quantize value using ceil
`qf`(v[, nbit])	Quantize value using floor
`qr`(v[, nbit])	Quantize value using round
`reverse_bin`(slbin, nreverse)	Reverse binary string list per nreverse value
`split_bin`(sbin, nbit)	Split sbin into many segment with the length nbit

bin2hex(data)[source]: Convert binary string list to hex string list

bin2hex_str(sbin)[source]: Convert binary string to hex string

check_dec(idec, nbit, signed=False, name='')[source]: Check whether the data (idec) is in the range range is $[0, 2^nbit-1]$ for unsigned range is $[-2^{nbit-1}, 2^{nbit-1}-1]$ for signed

dec2bin(idec, nbit=10, signed=False, name='')[source]: Convert dec array to binary string list

extend_bin(slbin, nfull)[source]

Extend the element of list (slbin) to the length (nfull)

such as, when

slbin = [‘10010’,’10100’],

nfull = 6

extent to

[‘010010’,’010100’]

extend_hex(slhex, nfull)[source]: Extend the element of list (slhex) to the length (nfull)

extend_list(slbin, nfull)[source]

Extend the list (slbin) to the length (nfull) the attched element of list is 0

such as, when

slbin = [‘10010’,’10100’],

nfull = 4

extent it to

[‘10010’,’10100’,’00000’,’00000]

hex2bin(data)[source]: Convert hex string list to binary string list

hex2bin_str(shex)[source]: Convert hex string to binary string

merge_bin(slbin, nmerge)[source]: Merge binary string list per nmerge value

qc(v, nbit: int = 14)[source]: Quantize value using ceil

qf(v, nbit: int = 14)[source]: Quantize value using floor

qr(v, nbit: int = 14)[source]: Quantize value using round

reverse_bin(slbin, nreverse)[source]: Reverse binary string list per nreverse value

split_bin(sbin, nbit: int)[source]: Split sbin into many segment with the length nbit

deepmd.nvnmd.utils.fio module

class deepmd.nvnmd.utils.fio.Fio[source]

Bases: object

Basic class for FIO

Methods

create_file_path
exits
get_file_list
is_file
is_path
mkdir

create_file_path(file_name='')[source]

exits(file_name='')[source]

get_file_list(path) → list[source]

is_file(file_name)[source]

is_path(path)[source]

mkdir(path_name='')[source]

class deepmd.nvnmd.utils.fio.FioBin[source]

Bases: object

Input and output for binary file

Methods

`load`([file_name, default_value])	Load binary file into bytes value
`save`([file_name, data])	Save hex string into binary file

load(file_name='', default_value='')[source]: Load binary file into bytes value

save(file_name: str = '', data: str = '')[source]: Save hex string into binary file

class deepmd.nvnmd.utils.fio.FioDic[source]

Bases: object

Input and output for dict class data the file can be .json or .npy file containing a dictionary

Methods

update(jdata, jdata_o)

Update key-value pair is key in jdata_o.keys()

get
load
save

get(jdata, key, default_value)[source]

load(file_name='', default_value={})[source]

save(file_name='', dic={})[source]

update(jdata, jdata_o)[source]: Update key-value pair is key in jdata_o.keys()

class deepmd.nvnmd.utils.fio.FioJsonDic[source]

Bases: object

Input and output for .json file containing dictionary

Methods

`load`([file_name, default_value])	Load .json file into dict
`save`([file_name, dic])	Save dict into .json file

load(file_name='', default_value={})[source]: Load .json file into dict

save(file_name='', dic={})[source]: Save dict into .json file

class deepmd.nvnmd.utils.fio.FioNpyDic[source]

Bases: object

Input and output for .npy file containing dictionary

Methods

load
save

load(file_name='', default_value={})[source]

save(file_name='', dic={})[source]

class deepmd.nvnmd.utils.fio.FioTxt[source]

Bases: object

Input and output for .txt file with string

Methods

`load`([file_name, default_value])	Load .txt file into string list
`save`([file_name, data])	Save string list into .txt file

load(file_name='', default_value=[])[source]: Load .txt file into string list

save(file_name: str = '', data: list = [])[source]: Save string list into .txt file

deepmd.nvnmd.utils.network module

deepmd.nvnmd.utils.network.get_sess()[source]

deepmd.nvnmd.utils.network.matmul2_qq(a, b, nbit)[source]: Quantized matmul operation for 2d tensor. a and b is input tensor, nbit represent quantification precision

deepmd.nvnmd.utils.network.matmul3_qq(a, b, nbit)[source]: Quantized matmul operation for 3d tensor. a and b is input tensor, nbit represent quantification precision

deepmd.nvnmd.utils.network.one_layer(inputs, outputs_size, activation_fn=<function tanh>, precision=tf.float64, stddev=1.0, bavg=0.0, name='linear', reuse=None, seed=None, use_timestep=False, trainable=True, useBN=False, uniform_seed=False, initial_variables=None, mixed_prec=None, final_layer=False)[source]: Build one layer with continuous or quantized value. Its weight and bias can be initialed with random or constant value.

deepmd.nvnmd.utils.network.one_layer_wb(shape, outputs_size, bavg, stddev, precision, trainable, initial_variables, seed, uniform_seed, name)[source]

deepmd.nvnmd.utils.network.qf(x, nbit)[source]: Quantize and floor tensor x with quantification precision nbit.

deepmd.nvnmd.utils.network.qr(x, nbit)[source]: Quantize and round tensor x with quantification precision nbit.

deepmd.nvnmd.utils.network.tanh2(x, nbit=- 1, nbit2=- 1)[source]: User-defined activation function tanh2

deepmd.nvnmd.utils.network.tanh4(x, nbit=- 1, nbit2=- 1)[source]: User-defined activation function tanh4

deepmd.nvnmd.utils.op module

deepmd.nvnmd.utils.op.map_nvnmd(x, map_y, map_dy, prec, nbit=None)[source]: Mapping function implemented by numpy

deepmd.nvnmd.utils.weight module

deepmd.nvnmd.utils.weight.get_constant_initializer(weights, name)[source]: Get initial value by name and create a initializer

deepmd.nvnmd.utils.weight.get_filter_weight(weights: dict, spe_i: int, spe_j: int, layer_l: int)[source]

Get weight and bias of embedding network

Parameters

spe_i(int): special order of central atom i 0~ntype-1
spe_j(int): special order of neighbor atom j 0~ntype-1
layer_l: layer order in embedding network 1~nlayer

deepmd.nvnmd.utils.weight.get_fitnet_weight(weights: dict, spe_i: int, layer_l: int, nlayer: int = 10)[source]

Get weight and bias of fitting network

Parameters

spe_i(int): special order of central atom i 0~ntype-1
layer_l(int): layer order in embedding network 0~nlayer-1

deepmd.nvnmd.utils.weight.get_normalize(weights: dict)[source]: Get normalize parameter (avg and std) of $s_{ji}$

deepmd.nvnmd.utils.weight.get_rng_s(weights: dict)[source]: Guess the range of $s_{ji}$

deepmd.nvnmd.utils.weight.get_weight(weights, key)[source]: Get weight value according to key

deepmd.op package

This module will house cust Tf OPs after CMake installation.

deepmd.op.import_ops()[source]

Import all custom TF ops that are present in this submodule.

Notes

Initialy this subdir is unpopulated. CMake will install all the op module python files and shared libs.

deepmd.train package

Submodules

deepmd.train.run_options module

Module taking care of important package constants.

class deepmd.train.run_options.RunOptions(init_model: Optional[str] = None, init_frz_model: Optional[str] = None, restart: Optional[str] = None, log_path: Optional[str] = None, log_level: int = 0, mpi_log: str = 'master')[source]

Bases: object

Class with inf oon how to run training (cluster, MPI and GPU config).

Attributes

gpus: Optional[List[int]]: list of GPUs if any are present else None
is_chief: bool: in distribured training it is true for tha main MPI process in serail it is always true
world_size: int: total worker count
my_rank: int: index of the MPI task
nodename: str: name of the node
node_list_List[str]: the list of nodes of the current mpirun
my_device: str: deviice type - gpu or cpu

Methods

print_resource_summary()

Print build and current running cluster configuration summary.

gpus: Optional[List[int]]

property is_chief: Whether my rank is 0.

my_device: str

my_rank: int

nodelist: List[int]

nodename: str

print_resource_summary()[source]: Print build and current running cluster configuration summary.

world_size: int

deepmd.train.trainer module

class deepmd.train.trainer.DPTrainer(jdata, run_opt, is_compress=False)[source]

Bases: object

Methods

save_compressed()

Save the compressed graph

build
get_evaluation_results
get_feed_dict
get_global_step
print_header
print_on_training
save_checkpoint
train
valid_on_the_fly

build(data=None, stop_batch=0, suffix='')[source]

get_evaluation_results(batch_list)[source]

get_feed_dict(batch, is_training)[source]

get_global_step()[source]

static print_header(fp, train_results, valid_results)[source]

static print_on_training(fp, train_results, valid_results, cur_batch, cur_lr)[source]

save_checkpoint(cur_batch: int)[source]

save_compressed()[source]: Save the compressed graph

train(train_data=None, valid_data=None)[source]

valid_on_the_fly(fp, train_batches, valid_batches, print_header=False)[source]

deepmd.utils package

Submodules

deepmd.utils.argcheck module

class deepmd.utils.argcheck.ArgsPlugin[source]

Bases: object

Methods

`get_all_argument`([exclude_hybrid])	Get all arguments.
`register`(name[, alias])	Regiester a descriptor argument plugin.

get_all_argument(exclude_hybrid: bool = False) → List[dargs.dargs.Argument][source]

Get all arguments.

Parameters

exclude_hybridbool: exclude hybrid descriptor to prevent circular calls

Returns

List[Argument]: all arguments

register(name: str, alias: Optional[List[str]] = None) → Callable[[], List[dargs.dargs.Argument]][source]

Regiester a descriptor argument plugin.

Parameters

namestr: the name of a descriptor
aliasList[str], optional: the list of aliases of this descriptor

Returns

Callable[[], List[Argument]]: the regiestered descriptor argument method

Examples

>>> some_plugin = ArgsPlugin()
>>> @some_plugin.register("some_descrpt")
    def descrpt_some_descrpt_args():
        return []

deepmd.utils.argcheck.descrpt_hybrid_args()[source]

deepmd.utils.argcheck.descrpt_local_frame_args()[source]

deepmd.utils.argcheck.descrpt_se_a_args()[source]

deepmd.utils.argcheck.descrpt_se_a_tpe_args()[source]

deepmd.utils.argcheck.descrpt_se_atten_args()[source]

deepmd.utils.argcheck.descrpt_se_r_args()[source]

deepmd.utils.argcheck.descrpt_se_t_args()[source]

deepmd.utils.argcheck.descrpt_variant_type_args(exclude_hybrid: bool = False) → dargs.dargs.Variant[source]

deepmd.utils.argcheck.fitting_dipole()[source]

deepmd.utils.argcheck.fitting_ener()[source]

deepmd.utils.argcheck.fitting_polar()[source]

deepmd.utils.argcheck.fitting_variant_type_args()[source]

deepmd.utils.argcheck.gen_doc(*, make_anchor=True, make_link=True, **kwargs)[source]

deepmd.utils.argcheck.gen_json(**kwargs)[source]

deepmd.utils.argcheck.learning_rate_args()[source]

deepmd.utils.argcheck.learning_rate_exp()[source]

deepmd.utils.argcheck.learning_rate_variant_type_args()[source]

deepmd.utils.argcheck.limit_pref(item)[source]

deepmd.utils.argcheck.list_to_doc(xx)[source]

deepmd.utils.argcheck.loss_args()[source]

deepmd.utils.argcheck.loss_ener()[source]

deepmd.utils.argcheck.loss_tensor()[source]

deepmd.utils.argcheck.loss_variant_type_args()[source]

deepmd.utils.argcheck.make_index(keys)[source]

deepmd.utils.argcheck.make_link(content, ref_key)[source]

deepmd.utils.argcheck.mixed_precision_args()[source]

deepmd.utils.argcheck.model_args()[source]

deepmd.utils.argcheck.model_compression()[source]

deepmd.utils.argcheck.model_compression_type_args()[source]

deepmd.utils.argcheck.modifier_dipole_charge()[source]

deepmd.utils.argcheck.modifier_variant_type_args()[source]

deepmd.utils.argcheck.normalize(data)[source]

deepmd.utils.argcheck.normalize_hybrid_list(hy_list)[source]

deepmd.utils.argcheck.start_pref(item)[source]

deepmd.utils.argcheck.training_args()[source]

deepmd.utils.argcheck.training_data_args()[source]

deepmd.utils.argcheck.type_embedding_args()[source]

deepmd.utils.argcheck.validation_data_args()[source]

deepmd.utils.batch_size module

class deepmd.utils.batch_size.AutoBatchSize(initial_batch_size: int = 1024, factor: float = 2.0)[source]

Bases: object

This class allows DeePMD-kit to automatically decide the maximum batch size that will not cause an OOM error.

Parameters

initial_batch_sizeint, default: 1024: initial batch size (number of total atoms)
factorfloat, default: 2.: increased factor

Notes

We assume all OOM error will raise OutOfMemoryError.

Attributes

current_batch_sizeint: current batch size (number of total atoms)
maximum_working_batch_sizeint: maximum working batch size
minimal_not_working_batch_sizeint: minimal not working batch size

Methods

`execute`(callable, start_index, natoms)	Excuate a method with given batch size.
`execute_all`(callable, total_size, natoms, ...)	Excuate a method with all given data.

execute(callable: Callable, start_index: int, natoms: int) → Tuple[int, tuple][source]

Excuate a method with given batch size.

Parameters

callableCallable: The method should accept the batch size and start_index as parameters, and returns executed batch size and data.
start_indexint: start index
natomsint: natoms

Returns

int: executed batch size * number of atoms
tuple: result from callable, None if failing to execute

Raises

OutOfMemoryError: OOM when batch size is 1

execute_all(callable: Callable, total_size: int, natoms: int, *args, **kwargs) → Tuple[numpy.ndarray][source]

Excuate a method with all given data.

Parameters

callableCallable: The method should accept *args and **kwargs as input and return the similiar array.
total_sizeint: Total size
natomsint: The number of atoms
**kwargs: If 2D np.ndarray, assume the first axis is batch; otherwise do nothing.

deepmd.utils.compat module

Module providing compatibility between 0.x.x and 1.x.x input versions.

deepmd.utils.compat.convert_input_v0_v1(jdata: Dict[str, Any], warning: bool = True, dump: Optional[Union[str, pathlib.Path]] = None) → Dict[str, Any][source]

Convert input from v0 format to v1.

Parameters

jdataDict[str, Any]: loaded json/yaml file
warningbool, optional: whether to show deprecation warning, by default True
dumpOptional[Union[str, Path]], optional: whether to dump converted file, by default None

Returns

Dict[str, Any]: converted output

deepmd.utils.compat.convert_input_v1_v2(jdata: Dict[str, Any], warning: bool = True, dump: Optional[Union[str, pathlib.Path]] = None) → Dict[str, Any][source]

deepmd.utils.compat.deprecate_numb_test(jdata: Dict[str, Any], warning: bool = True, dump: Optional[Union[str, pathlib.Path]] = None) → Dict[str, Any][source]

Deprecate numb_test since v2.1. It has taken no effect since v2.0.

See #1243.

Parameters

jdataDict[str, Any]: loaded json/yaml file
warningbool, optional: whether to show deprecation warning, by default True
dumpOptional[Union[str, Path]], optional: whether to dump converted file, by default None

Returns

Dict[str, Any]: converted output

deepmd.utils.compat.remove_decay_rate(jdata: Dict[str, Any])[source]

convert decay_rate to stop_lr.

Parameters

jdata: Dict[str, Any]: input data

deepmd.utils.compat.update_deepmd_input(jdata: Dict[str, Any], warning: bool = True, dump: Optional[Union[str, pathlib.Path]] = None) → Dict[str, Any][source]

deepmd.utils.convert module

deepmd.utils.convert.convert_012_to_21(input_model: str, output_model: str)[source]

Convert DP 0.12 graph to 2.1 graph.

Parameters

input_modelstr: filename of the input graph
output_modelstr: filename of the output graph

deepmd.utils.convert.convert_10_to_21(input_model: str, output_model: str)[source]

Convert DP 1.0 graph to 2.1 graph.

Parameters

input_modelstr: filename of the input graph
output_modelstr: filename of the output graph

deepmd.utils.convert.convert_12_to_21(input_model: str, output_model: str)[source]

Convert DP 1.2 graph to 2.1 graph.

Parameters

input_modelstr: filename of the input graph
output_modelstr: filename of the output graph

deepmd.utils.convert.convert_13_to_21(input_model: str, output_model: str)[source]

Convert DP 1.3 graph to 2.1 graph.

Parameters

input_modelstr: filename of the input graph
output_modelstr: filename of the output graph

deepmd.utils.convert.convert_20_to_21(input_model: str, output_model: str)[source]

Convert DP 2.0 graph to 2.1 graph.

Parameters

input_modelstr: filename of the input graph
output_modelstr: filename of the output graph

deepmd.utils.convert.convert_dp012_to_dp10(file: str)[source]

Convert DP 1.0 graph text to 1.1 graph text.

Parameters

filestr: filename of the graph text

deepmd.utils.convert.convert_dp10_to_dp11(file: str)[source]

Convert DP 1.0 graph text to 1.1 graph text.

Parameters

filestr: filename of the graph text

deepmd.utils.convert.convert_dp12_to_dp13(file: str)[source]

Convert DP 1.2 graph text to 1.3 graph text.

Parameters

filestr: filename of the graph text

deepmd.utils.convert.convert_dp13_to_dp20(fname: str)[source]

Convert DP 1.3 graph text to 2.0 graph text.

Parameters

filestr: filename of the graph text

deepmd.utils.convert.convert_dp20_to_dp21(fname: str)[source]

deepmd.utils.convert.convert_pb_to_pbtxt(pbfile: str, pbtxtfile: str)[source]

Convert DP graph to graph text.

Parameters

pbfilestr: filename of the input graph
pbtxtfilestr: filename of the output graph text

deepmd.utils.convert.convert_pbtxt_to_pb(pbtxtfile: str, pbfile: str)[source]

Convert DP graph text to graph.

Parameters

pbtxtfilestr: filename of the input graph text
pbfilestr: filename of the output graph

deepmd.utils.data module

class deepmd.utils.data.DataSets(sys_path, set_prefix, seed=None, shuffle_test=True)[source]

Bases: object

Outdated class for one data system.

Deprecated since version 2.0.0: This class is not maintained any more.

Methods

`get_batch`(batch_size)	returned property prefector [4] in order: energy, force, virial, atom_ener
`get_test`()	returned property prefector [4] in order: energy, force, virial, atom_ener
`load_energy`(set_name, nframes, nvalues, ...)	return : coeff_ener, ener, coeff_atom_ener, atom_ener

check_batch_size
check_test_size
get_ener
get_natoms
get_natoms_2
get_natoms_vec
get_numb_set
get_set
get_sys_numb_batch
get_type_map
load_batch_set
load_data
load_set
load_test_set
load_type
load_type_map
numb_aparam
numb_fparam
reset_iter
set_numb_batch
stats_energy

check_batch_size(batch_size)[source]

check_test_size(test_size)[source]

get_batch(batch_size)[source]: returned property prefector [4] in order: energy, force, virial, atom_ener

get_ener()[source]

get_natoms()[source]

get_natoms_2(ntypes)[source]

get_natoms_vec(ntypes)[source]

get_numb_set()[source]

get_set(data, idx=None)[source]

get_sys_numb_batch(batch_size)[source]

get_test()[source]: returned property prefector [4] in order: energy, force, virial, atom_ener

get_type_map()[source]

load_batch_set(set_name)[source]

load_data(set_name, data_name, shape, is_necessary=True)[source]

load_energy(set_name, nframes, nvalues, energy_file, atom_energy_file)[source]: return : coeff_ener, ener, coeff_atom_ener, atom_ener

load_set(set_name, shuffle=True)[source]

load_test_set(set_name, shuffle_test)[source]

load_type(sys_path)[source]

load_type_map(sys_path)[source]

numb_aparam()[source]

numb_fparam()[source]

reset_iter()[source]

set_numb_batch(batch_size)[source]

stats_energy()[source]

class deepmd.utils.data.DeepmdData(sys_path: str, set_prefix: str = 'set', shuffle_test: bool = True, type_map: Optional[List[str]] = None, modifier=None, trn_all_set: bool = False)[source]

Bases: object

Class for a data system.

It loads data from hard disk, and mantains the data as a data_dict

Parameters

sys_path: Path to the data system
set_prefix: Prefix for the directories of different sets
shuffle_test: If the test data are shuffled
type_map: Gives the name of different atom types
modifier: Data modifier that has the method modify_data
trn_all_set: Use all sets as training dataset. Otherwise, if the number of sets is more than 1, the last set is left for test.

Methods

`add`(key, ndof[, atomic, must, high_prec, ...])	Add a data item that to be loaded
`avg`(key)	Return the average value of an item.
`check_batch_size`(batch_size)	Check if the system can get a batch of data with batch_size frames.
`check_test_size`(test_size)	Check if the system can get a test dataset with test_size frames.
`get_atom_type`()	Get atom types
`get_batch`(batch_size)	Get a batch of data with batch_size frames.
`get_data_dict`()	Get the data_dict
`get_natoms`()	Get number of atoms
`get_natoms_vec`(ntypes)	Get number of atoms and number of atoms in different types
`get_ntypes`()	Number of atom types in the system
`get_numb_batch`(batch_size, set_idx)	Get the number of batches in a set.
`get_numb_set`()	Get number of training sets
`get_sys_numb_batch`(batch_size)	Get the number of batches in the data system.
`get_test`([ntests])	Get the test data with ntests frames.
`get_type_map`()	Get the type map
`reduce`(key_out, key_in)	Generate a new item from the reduction of another atom

reset_get_batch

add(key: str, ndof: int, atomic: bool = False, must: bool = False, high_prec: bool = False, type_sel: Optional[List[int]] = None, repeat: int = 1, default: float = 0.0)[source]

Add a data item that to be loaded

Parameters

key: The key of the item. The corresponding data is stored in sys_path/set.*/key.npy
ndof: The number of dof
atomic: The item is an atomic property. If False, the size of the data should be nframes x ndof If True, the size of data should be nframes x natoms x ndof
must: The data file sys_path/set.*/key.npy must exist. If must is False and the data file does not exist, the data_dict[find_key] is set to 0.0
high_prec: Load the data and store in float64, otherwise in float32
type_sel: Select certain type of atoms
repeat: The data will be repeated repeat times.
defaultfloat, default=0.: default value of data

avg(key)[source]: Return the average value of an item.

check_batch_size(batch_size)[source]: Check if the system can get a batch of data with batch_size frames.

check_test_size(test_size)[source]: Check if the system can get a test dataset with test_size frames.

get_atom_type() → List[int][source]: Get atom types

get_batch(batch_size: int) → dict[source]

Get a batch of data with batch_size frames. The frames are randomly picked from the data system.

Parameters

batch_size: size of the batch

get_data_dict() → dict[source]: Get the data_dict

get_natoms()[source]: Get number of atoms

get_natoms_vec(ntypes: int)[source]

Get number of atoms and number of atoms in different types

Parameters

ntypes: Number of types (may be larger than the actual number of types in the system).

Returns

natoms: natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

get_ntypes() → int[source]: Number of atom types in the system

get_numb_batch(batch_size: int, set_idx: int) → int[source]: Get the number of batches in a set.

get_numb_set() → int[source]: Get number of training sets

get_sys_numb_batch(batch_size: int) → int[source]: Get the number of batches in the data system.

get_test(ntests: int = - 1) → dict[source]

Get the test data with ntests frames.

Parameters

ntests: Size of the test data set. If ntests is -1, all test data will be get.

get_type_map() → List[str][source]: Get the type map

reduce(key_out: str, key_in: str)[source]

Generate a new item from the reduction of another atom

Parameters

key_out: The name of the reduced item
key_in: The name of the data item to be reduced

reset_get_batch()[source]

deepmd.utils.data_system module

class deepmd.utils.data_system.DataSystem(systems, set_prefix, batch_size, test_size, rcut, run_opt=None)[source]

Bases: object

Outdated class for the data systems.

Deprecated since version 2.0.0: This class is not maintained any more.

Methods

check_type_map_consistency
compute_energy_shift
format_name_length
get_batch
get_batch_size
get_nbatches
get_nsystems
get_ntypes
get_sys
get_test
get_type_map
numb_fparam
print_summary
process_sys_weights

check_type_map_consistency(type_map_list)[source]

compute_energy_shift()[source]

format_name_length(name, width)[source]

get_batch(sys_idx=None, sys_weights=None, style='prob_sys_size')[source]

get_batch_size()[source]

get_nbatches()[source]

get_nsystems()[source]

get_ntypes()[source]

get_sys(sys_idx)[source]

get_test(sys_idx=None)[source]

get_type_map()[source]

numb_fparam()[source]

print_summary()[source]

process_sys_weights(sys_weights)[source]

class deepmd.utils.data_system.DeepmdDataSystem(systems: List[str], batch_size: int, test_size: int, rcut: float, set_prefix: str = 'set', shuffle_test: bool = True, type_map: Optional[List[str]] = None, modifier=None, trn_all_set=False, sys_probs=None, auto_prob_style='prob_sys_size')[source]

Bases: object

Class for manipulating many data systems.

It is implemented with the help of DeepmdData

Methods

`add`(key, ndof[, atomic, must, high_prec, ...])	Add a data item that to be loaded
`add_dict`(adict)	Add items to the data system by a dict.
`get_batch`([sys_idx])	Get a batch of data from the data systems
`get_batch_size`()	Get the batch size
`get_nbatches`()	Get the total number of batches
`get_nsystems`()	Get the number of data systems
`get_ntypes`()	Get the number of types
`get_sys`(idx)	Get a certain data system
`get_sys_ntest`([sys_idx])	Get number of tests for the currently selected system,
`get_test`([sys_idx, n_test])	Get test data from the the data systems.
`get_type_map`()	Get the type map
`reduce`(key_out, key_in)	Generate a new item from the reduction of another atom

compute_energy_shift
get_data_dict
print_summary
set_sys_probs

add(key: str, ndof: int, atomic: bool = False, must: bool = False, high_prec: bool = False, type_sel: Optional[List[int]] = None, repeat: int = 1, default: float = 0.0)[source]

Add a data item that to be loaded

Parameters

key: The key of the item. The corresponding data is stored in sys_path/set.*/key.npy
ndof: The number of dof
atomic: The item is an atomic property. If False, the size of the data should be nframes x ndof If True, the size of data should be nframes x natoms x ndof
must: The data file sys_path/set.*/key.npy must exist. If must is False and the data file does not exist, the data_dict[find_key] is set to 0.0
high_prec: Load the data and store in float64, otherwise in float32
type_sel: Select certain type of atoms
repeat: The data will be repeated repeat times.
default, default=0.: Default value of data

add_dict(adict: dict) → None[source]

Add items to the data system by a dict. adict should have items like .. code-block:: python

adict[key] = {
‘ndof’: ndof, ‘atomic’: atomic, ‘must’: must, ‘high_prec’: high_prec, ‘type_sel’: type_sel, ‘repeat’: repeat,

}

For the explaination of the keys see add

compute_energy_shift(rcond=0.001, key='energy')[source]

get_batch(sys_idx: Optional[int] = None)[source]

Get a batch of data from the data systems

Parameters

sys_idx: int: The index of system from which the batch is get. If sys_idx is not None, sys_probs and auto_prob_style are ignored If sys_idx is None, automatically determine the system according to sys_probs or auto_prob_style, see the following.

get_batch_size() → int[source]: Get the batch size

get_data_dict(ii: int = 0) → dict[source]

get_nbatches() → int[source]: Get the total number of batches

get_nsystems() → int[source]: Get the number of data systems

get_ntypes() → int[source]: Get the number of types

get_sys(idx: int) → deepmd.utils.data.DeepmdData[source]: Get a certain data system

get_sys_ntest(sys_idx=None)[source]

Get number of tests for the currently selected system,: or one defined by sys_idx.

get_test(sys_idx: Optional[int] = None, n_test: int = - 1)[source]

Get test data from the the data systems.

Parameters

sys_idx: The test dat of system with index sys_idx will be returned. If is None, the currently selected system will be returned.
n_test: Number of test data. If set to -1 all test data will be get.

get_type_map() → List[str][source]: Get the type map

print_summary(name)[source]

reduce(key_out, key_in)[source]

Generate a new item from the reduction of another atom

Parameters

key_out: The name of the reduced item
key_in: The name of the data item to be reduced

set_sys_probs(sys_probs=None, auto_prob_style: str = 'prob_sys_size')[source]

deepmd.utils.errors module

exception deepmd.utils.errors.GraphTooLargeError[source]

Bases: Exception

The graph is too large, exceeding protobuf’s hard limit of 2GB.

exception deepmd.utils.errors.GraphWithoutTensorError[source]: Bases: Exception

exception deepmd.utils.errors.OutOfMemoryError[source]

Bases: Exception

This error is caused by out-of-memory (OOM).

deepmd.utils.graph module

deepmd.utils.graph.get_attention_layer_nodes_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the attention layer nodes with the given tf.GraphDef object

Parameters

graph_def: The input tf.GraphDef object
suffixstr, optional: The scope suffix

Returns

Dict: The attention layer nodes within the given tf.GraphDef object

deepmd.utils.graph.get_attention_layer_variables_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the attention layer variables with the given tf.GraphDef object

Parameters

graph_deftf.GraphDef: The input tf.GraphDef object
suffixstr, optional: The suffix of the scope

Returns

Dict: The attention layer variables within the given tf.GraphDef object

deepmd.utils.graph.get_embedding_net_nodes(model_file: str, suffix: str = '') → Dict[source]

Get the embedding net nodes with the given frozen model(model_file)

Parameters

model_file: The input frozen model path
suffixstr, optional: The suffix of the scope

Returns

Dict: The embedding net nodes with the given frozen model

deepmd.utils.graph.get_embedding_net_nodes_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the embedding net nodes with the given tf.GraphDef object

Parameters

graph_def: The input tf.GraphDef object
suffixstr, optional: The scope suffix

Returns

Dict: The embedding net nodes within the given tf.GraphDef object

deepmd.utils.graph.get_embedding_net_variables(model_file: str, suffix: str = '') → Dict[source]

Get the embedding net variables with the given frozen model(model_file)

Parameters

model_file: The input frozen model path
suffixstr, optional: The suffix of the scope

Returns

Dict: The embedding net variables within the given frozen model

deepmd.utils.graph.get_embedding_net_variables_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the embedding net variables with the given tf.GraphDef object

Parameters

graph_def: The input tf.GraphDef object
suffixstr, optional: The suffix of the scope

Returns

Dict: The embedding net variables within the given tf.GraphDef object

deepmd.utils.graph.get_fitting_net_nodes(model_file: str) → Dict[source]

Get the fitting net nodes with the given frozen model(model_file)

Parameters

model_file: The input frozen model path

Returns

Dict: The fitting net nodes with the given frozen model

deepmd.utils.graph.get_fitting_net_nodes_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the fitting net nodes with the given tf.GraphDef object

Parameters

graph_def: The input tf.GraphDef object
suffix: suffix of the scope

Returns

Dict: The fitting net nodes within the given tf.GraphDef object

deepmd.utils.graph.get_fitting_net_variables(model_file: str, suffix: str = '') → Dict[source]

Get the fitting net variables with the given frozen model(model_file)

Parameters

model_file: The input frozen model path
suffix: suffix of the scope

Returns

Dict: The fitting net variables within the given frozen model

deepmd.utils.graph.get_fitting_net_variables_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the fitting net variables with the given tf.GraphDef object

Parameters

graph_def: The input tf.GraphDef object
suffix: suffix of the scope

Returns

Dict: The fitting net variables within the given tf.GraphDef object

deepmd.utils.graph.get_pattern_nodes_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, pattern: str) → Dict[source]

Get the pattern nodes with the given tf.GraphDef object

Parameters

graph_def: The input tf.GraphDef object
pattern: The node pattern within the graph_def

Returns

Dict: The fitting net nodes within the given tf.GraphDef object

deepmd.utils.graph.get_tensor_by_name(model_file: str, tensor_name: str) → tensorflow.python.framework.ops.Tensor[source]

Load tensor value from the frozen model(model_file)

Parameters

model_filestr: The input frozen model path
tensor_namestr: Indicates which tensor which will be loaded from the frozen model

Returns

tf.Tensor: The tensor which was loaded from the frozen model

Raises

GraphWithoutTensorError: Whether the tensor_name is within the frozen model

deepmd.utils.graph.get_tensor_by_name_from_graph(graph: tensorflow.python.framework.ops.Graph, tensor_name: str) → tensorflow.python.framework.ops.Tensor[source]

Load tensor value from the given tf.Graph object

Parameters

graphtf.Graph: The input TensorFlow graph
tensor_namestr: Indicates which tensor which will be loaded from the frozen model

Returns

tf.Tensor: The tensor which was loaded from the frozen model

Raises

GraphWithoutTensorError: Whether the tensor_name is within the frozen model

deepmd.utils.graph.get_tensor_by_type(node, data_type: numpy.dtype) → tensorflow.python.framework.ops.Tensor[source]

Get the tensor value within the given node according to the input data_type

Parameters

node: The given tensorflow graph node
data_type: The data type of the node

Returns

tf.Tensor: The tensor value of the given node

deepmd.utils.graph.get_type_embedding_net_nodes_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the type embedding net nodes with the given tf.GraphDef object

Parameters

graph_def: The input tf.GraphDef object
suffixstr, optional: The scope suffix

Returns

Dict: The type embedding net nodes within the given tf.GraphDef object

deepmd.utils.graph.get_type_embedding_net_variables_from_graph_def(graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix: str = '') → Dict[source]

Get the type embedding net variables with the given tf.GraphDef object

Parameters

graph_deftf.GraphDef: The input tf.GraphDef object
suffixstr, optional: The suffix of the scope

Returns

Dict: The embedding net variables within the given tf.GraphDef object

deepmd.utils.graph.load_graph_def(model_file: str) → Tuple[tensorflow.python.framework.ops.Graph, tensorflow.core.framework.graph_pb2.GraphDef][source]

Load graph as well as the graph_def from the frozen model(model_file)

Parameters

model_filestr: The input frozen model path

Returns

tf.Graph: The graph loaded from the frozen model
tf.GraphDef: The graph_def loaded from the frozen model

deepmd.utils.learning_rate module

class deepmd.utils.learning_rate.LearningRateExp(start_lr: float, stop_lr: float = 5e-08, decay_steps: int = 5000, decay_rate: float = 0.95)[source]

Bases: object

The exponentially decaying learning rate.

The learning rate at step $t$ is given by

\[\alpha(t) = \alpha_0 \lambda ^ { t / \tau }\]

where $\alpha$ is the learning rate, $\alpha_0$ is the starting learning rate, $\lambda$ is the decay rate, and $\tau$ is the decay steps.

Parameters

start_lr: Starting learning rate $\alpha_0$
stop_lr: Stop learning rate $\alpha_1$
decay_steps: Learning rate decay every this number of steps $\tau$
decay_rate: The decay rate $\lambda$. If stop_step is provided in build, then it will be determined automatically and overwritten.

Methods

`build`(global_step[, stop_step])	Build the learning rate
`start_lr`()	Get the start lr
`value`(step)	Get the lr at a certain step

build(global_step: tensorflow.python.framework.ops.Tensor, stop_step: Optional[int] = None) → tensorflow.python.framework.ops.Tensor[source]

Build the learning rate

Parameters

global_step: The tf Tensor prividing the global training step
stop_step: The stop step. If provided, the decay_rate will be determined automatically and overwritten.

Returns

learning_rate: The learning rate

start_lr() → float[source]: Get the start lr

value(step: int) → float[source]: Get the lr at a certain step

deepmd.utils.neighbor_stat module

class deepmd.utils.neighbor_stat.NeighborStat(ntypes: int, rcut: float, one_type: bool = False)[source]

Bases: object

Class for getting training data information.

It loads data from DeepmdData object, and measures the data info, including neareest nbor distance between atoms, max nbor size of atoms and the output data range of the environment matrix.

Parameters

ntypes: The num of atom types
rcut: The cut-off radius
one_typebool, optional, default=False: Treat all types as a single type.

Methods

get_stat(data)

get the data statistics of the training data, including nearest nbor distance between atoms, max nbor size of atoms

get_stat(data: deepmd.utils.data_system.DeepmdDataSystem) → Tuple[float, List[int]][source]

get the data statistics of the training data, including nearest nbor distance between atoms, max nbor size of atoms

Parameters

data: Class for manipulating many data systems. It is implemented with the help of DeepmdData.

Returns

min_nbor_dist: The nearest distance between neighbor atoms
max_nbor_size: A list with ntypes integers, denotes the actual achieved max sel

deepmd.utils.network module

deepmd.utils.network.embedding_net(xx, network_size, precision, activation_fn=<function tanh>, resnet_dt=False, name_suffix='', stddev=1.0, bavg=0.0, seed=None, trainable=True, uniform_seed=False, initial_variables=None, mixed_prec=None)[source]

The embedding network.

The embedding network function $\mathcal{N}$ is constructed by is the composition of multiple layers $\mathcal{L}^{(i)}$:

\[\mathcal{N} = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)} \circ \cdots \circ \mathcal{L}^{(1)}\]

A layer $\mathcal{L}$ is given by one of the following forms, depending on the number of nodes: [1]

\[\begin{split}\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= \begin{cases} \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + \mathbf{x}, & N_2=N_1 \\ \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + (\mathbf{x}, \mathbf{x}), & N_2 = 2N_1\\ \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}), & \text{otherwise} \\ \end{cases}\end{split}\]

where $\mathbf{x} \in \mathbb{R}^{N_1}$ is the input vector and $\mathbf{y} \in \mathbb{R}^{N_2}$ is the output vector. $\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}$ and $\mathbf{b} \in \mathbb{R}^{N_2}$ are weights and biases, respectively, both of which are trainable if trainable is True. $\boldsymbol{\phi}$ is the activation function.

Parameters

xxTensor: Input tensor $\mathbf{x}$ of shape [-1,1]
network_size: list of int: Size of the embedding network. For example [16,32,64]
precision:: Precision of network weights. For example, tf.float64
activation_fn:: Activation function $\boldsymbol{\phi}$
resnet_dt: boolean: Using time-step in the ResNet construction
name_suffix: str: The name suffix append to each variable.
stddev: float: Standard deviation of initializing network parameters
bavg: float: Mean of network intial bias
seed: int: Random seed for initializing network parameters
trainable: boolean: If the network is trainable
uniform_seedbool: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
initial_variablesdict: The input dict which stores the embedding net variables
mixed_prec: The input dict which stores the mixed precision setting for the embedding net

References

1: Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identitymappings in deep residual networks. InComputer Vision – ECCV 2016,pages 630–645. Springer International Publishing, 2016.

deepmd.utils.network.embedding_net_rand_seed_shift(network_size)[source]

deepmd.utils.network.one_layer(inputs, outputs_size, activation_fn=<function tanh>, precision=tf.float64, stddev=1.0, bavg=0.0, name='linear', scope='', reuse=None, seed=None, use_timestep=False, trainable=True, useBN=False, uniform_seed=False, initial_variables=None, mixed_prec=None, final_layer=False)[source]

deepmd.utils.network.one_layer_rand_seed_shift()[source]

deepmd.utils.network.variable_summaries(var: tensorflow.python.ops.variables.VariableV1, name: str)[source]

Attach a lot of summaries to a Tensor (for TensorBoard visualization).

Parameters

vartf.Variable: [description]
namestr: variable name

deepmd.utils.pair_tab module

class deepmd.utils.pair_tab.PairTab(filename: str)[source]

Bases: object

Parameters

filename: File name for the short-range tabulated potential. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.

Methods

`get`()	Get the serialized table.
`reinit`(filename)	Initialize the tabulated interaction

get() → Tuple[numpy.array, numpy.array][source]: Get the serialized table.

reinit(filename: str) → None[source]

Initialize the tabulated interaction

Parameters

filename: File name for the short-range tabulated potential. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.

deepmd.utils.parallel_op module

class deepmd.utils.parallel_op.ParallelOp(builder: Callable[[...], Tuple[Dict[str, tensorflow.python.framework.ops.Tensor], Tuple[tensorflow.python.framework.ops.Tensor]]], nthreads: Optional[int] = None, config: Optional[tensorflow.core.protobuf.config_pb2.ConfigProto] = None)[source]

Bases: object

Run an op with data parallelism.

Parameters

builderCallable[…, Tuple[Dict[str, tf.Tensor], Tuple[tf.Tensor]]]: returns two objects: a dict which stores placeholders by key, and a tuple with the final op(s)
nthreadsint, optional: the number of threads
configtf.ConfigProto, optional: tf.ConfigProto

Examples

>>> from deepmd.env import tf
>>> from deepmd.utils.parallel_op import ParallelOp
>>> def builder():
...     x = tf.placeholder(tf.int32, [1])
...     return {"x": x}, (x + 1)
...
>>> p = ParallelOp(builder, nthreads=4)
>>> def feed():
...     for ii in range(10):
...         yield {"x": [ii]}
...
>>> print(*p.generate(tf.Session(), feed()))
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Methods

generate(sess, feed)

Returns a generator.

generate(sess: tensorflow.python.client.session.Session, feed: Generator[Dict[str, Any], None, None]) → Generator[Tuple, None, None][source]

Returns a generator.

Parameters

feedGenerator[dict, None, None]: generator which yields feed_dict

Yields

Generator[Tuple, None, None]: generator which yields session returns

deepmd.utils.path module

class deepmd.utils.path.DPH5Path(path: str)[source]

Bases: deepmd.utils.path.DPPath

The path class to data system (DeepmdData) for HDF5 files.

Parameters

pathstr: path

Notes

OS - HDF5 relationship:: directory - Group file - Dataset

Methods

`glob`(pattern)	Search path using the glob pattern.
`is_dir`()	Check if self is directory.
`is_file`()	Check if self is file.
`load_numpy`()	Load NumPy array.
`load_txt`([dtype])	Load NumPy array from text.
`rglob`(pattern)	This is like calling `DPPath.glob()` with **/ added in front of the given relative pattern.

glob(pattern: str) → List[deepmd.utils.path.DPPath][source]

Search path using the glob pattern.

Parameters

patternstr: glob pattern

Returns

List[DPPath]: list of paths

is_dir() → bool[source]: Check if self is directory.

is_file() → bool[source]: Check if self is file.

load_numpy() → numpy.ndarray[source]

Load NumPy array.

Returns

np.ndarray: loaded NumPy array

load_txt(dtype: Optional[numpy.dtype] = None, **kwargs) → numpy.ndarray[source]

Load NumPy array from text.

Returns

np.ndarray: loaded NumPy array

rglob(pattern: str) → List[deepmd.utils.path.DPPath][source]

This is like calling DPPath.glob() with **/ added in front of the given relative pattern.

Parameters

patternstr: glob pattern

Returns

List[DPPath]: list of paths

class deepmd.utils.path.DPOSPath(path: str)[source]

Bases: deepmd.utils.path.DPPath

The OS path class to data system (DeepmdData) for real directories.

Parameters

pathstr: path

Methods

`glob`(pattern)	Search path using the glob pattern.
`is_dir`()	Check if self is directory.
`is_file`()	Check if self is file.
`load_numpy`()	Load NumPy array.
`load_txt`(**kwargs)	Load NumPy array from text.
`rglob`(pattern)	This is like calling `DPPath.glob()` with **/ added in front of the given relative pattern.

glob(pattern: str) → List[deepmd.utils.path.DPPath][source]

Search path using the glob pattern.

Parameters

patternstr: glob pattern

Returns

List[DPPath]: list of paths

is_dir() → bool[source]: Check if self is directory.

is_file() → bool[source]: Check if self is file.

load_numpy() → numpy.ndarray[source]

Load NumPy array.

Returns

np.ndarray: loaded NumPy array

load_txt(**kwargs) → numpy.ndarray[source]

Load NumPy array from text.

Returns

np.ndarray: loaded NumPy array

rglob(pattern: str) → List[deepmd.utils.path.DPPath][source]

This is like calling DPPath.glob() with **/ added in front of the given relative pattern.

Parameters

patternstr: glob pattern

Returns

List[DPPath]: list of paths

class deepmd.utils.path.DPPath(path: str)[source]

Bases: abc.ABC

The path class to data system (DeepmdData).

Parameters

pathstr: path

Methods

`glob`(pattern)	Search path using the glob pattern.
`is_dir`()	Check if self is directory.
`is_file`()	Check if self is file.
`load_numpy`()	Load NumPy array.
`load_txt`(**kwargs)	Load NumPy array from text.
`rglob`(pattern)	This is like calling `DPPath.glob()` with **/ added in front of the given relative pattern.

abstract glob(pattern: str) → List[deepmd.utils.path.DPPath][source]

Search path using the glob pattern.

Parameters

patternstr: glob pattern

Returns

List[DPPath]: list of paths

abstract is_dir() → bool[source]: Check if self is directory.

abstract is_file() → bool[source]: Check if self is file.

abstract load_numpy() → numpy.ndarray[source]

Load NumPy array.

Returns

np.ndarray: loaded NumPy array

abstract load_txt(**kwargs) → numpy.ndarray[source]

Load NumPy array from text.

Returns

np.ndarray: loaded NumPy array

abstract rglob(pattern: str) → List[deepmd.utils.path.DPPath][source]

This is like calling DPPath.glob() with **/ added in front of the given relative pattern.

Parameters

patternstr: glob pattern

Returns

List[DPPath]: list of paths

deepmd.utils.plugin module

Base of plugin systems.

class deepmd.utils.plugin.Plugin[source]

Bases: object

A class to register and restore plugins.

Examples

>>> plugin = Plugin()
>>> @plugin.register("xx")
    def xxx():
        pass
>>> print(plugin.plugins['xx'])

Attributes

pluginsDict[str, object]: plugins

Methods

`get_plugin`(key)	Visit a plugin by key.
`register`(key)	Register a plugin.

get_plugin(key) → object[source]

Visit a plugin by key.

Parameters

keystr: key of the plugin

Returns

object: the plugin

register(key: str) → Callable[[object], object][source]

Register a plugin.

Parameters

keystr: key of the plugin

Returns

Callable[[object], object]: decorator

class deepmd.utils.plugin.PluginVariant(*args, **kwargs)[source]

Bases: object

A class to remove type from input arguments.

class deepmd.utils.plugin.VariantABCMeta(name, bases, namespace, **kwargs)[source]

Bases: deepmd.utils.plugin.VariantMeta, abc.ABCMeta

Methods

`__call__`(args, *kwargs)	Remove type and keys that starts with underline.
`mro`(/)	Return a type's method resolution order.
`register`(subclass)	Register a virtual subclass of an ABC.

class deepmd.utils.plugin.VariantMeta[source]

Bases: object

Methods

__call__(*args, **kwargs)

Remove type and keys that starts with underline.

deepmd.utils.random module

deepmd.utils.random.choice(a: numpy.ndarray, p: Optional[numpy.ndarray] = None)[source]

Generates a random sample from a given 1-D array.

Parameters

anp.ndarray: A random sample is generated from its elements.
pnp.ndarray: The probabilities associated with each entry in a.

Returns

np.ndarray: arrays with results and their shapes

deepmd.utils.random.random(size=None)[source]

Return random floats in the half-open interval [0.0, 1.0).

Parameters

size: Output shape.

Returns

np.ndarray: Arrays with results and their shapes.

deepmd.utils.random.seed(val: Optional[int] = None)[source]

Seed the generator.

Parameters

valint: Seed.

deepmd.utils.random.shuffle(x: numpy.ndarray)[source]

Modify a sequence in-place by shuffling its contents.

Parameters

xnp.ndarray: The array or list to be shuffled.

deepmd.utils.sess module

deepmd.utils.sess.run_sess(sess: tensorflow.python.client.session.Session, *args, **kwargs)[source]

Run session with erorrs caught.

Parameters

sess: tf.Session: TensorFlow Session

Returns

the result of sess.run()

deepmd.utils.tabulate module

class deepmd.utils.tabulate.DPTabulate(descrpt: deepmd.descriptor.descriptor.Descriptor, neuron: typing.List[int], model_file: str, type_one_side: bool = False, exclude_types: typing.List[typing.List[int]] = [], activation_fn: typing.Callable[[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor] = <function tanh>, suffix: str = '')[source]

Bases: object

Class for tabulation.

Compress a model, which including tabulating the embedding-net. The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first table takes the stride(parameter) as it’s uniform stride, while the second table takes 10 * stride as its uniform stride The range of the first table is automatically detected by deepmd-kit, while the second table ranges from the first table’s upper boundary(upper) to the extrapolate(parameter) * upper.

Parameters

descrpt: Descriptor of the original model
neuron: Number of neurons in each hidden layers of the embedding net $\mathcal{N}$
model_file: The frozen model
type_one_side: Try to build N_types tables. Otherwise, building N_types^2 tables
exclude_typesList[List[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.
activation_function: The activation function in the embedding net. Supported options are {“tanh”,”gelu”} in common.ACTIVATION_FN_DICT.
suffixstr, optional: The suffix of the scope

Methods

build(min_nbor_dist, extrapolate, stride0, ...)

Build the tables for model compression

build(min_nbor_dist: float, extrapolate: float, stride0: float, stride1: float) → Tuple[Dict[str, int], Dict[str, int]][source]

Build the tables for model compression

Parameters

min_nbor_dist: The nearest distance between neighbor atoms
extrapolate: The scale of model extrapolation
stride0: The uniform stride of the first table
stride1: The uniform stride of the second table
neuron: Number of neurons in each hidden layers of the embedding net $\mathcal{N}$

Returns

lowerdict[str, int]: The lower boundary of environment matrix by net
upperdict[str, int]: The upper boundary of environment matrix by net

deepmd.utils.type_embed module

class deepmd.utils.type_embed.TypeEmbedNet(neuron: List[int] = [], resnet_dt: bool = False, activation_function: Optional[str] = 'tanh', precision: str = 'default', trainable: bool = True, seed: Optional[int] = None, uniform_seed: bool = False, padding: bool = False)[source]

Bases: object

Parameters

neuronlist[int]: Number of neurons in each hidden layers of the embedding net
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
activation_function: The activation function in the embedding net. Supported options are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”, “gelu_tf”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float16”, “float32”, “float64”.
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
padding: Concat the zero padding to the output, as the default embedding of empty type.

Methods

`build`(ntypes[, reuse, suffix])	Build the computational graph for the descriptor
`init_variables`(graph, graph_def[, suffix])	Init the type embedding net variables with the given dict

build(ntypes: int, reuse=None, suffix='')[source]

Build the computational graph for the descriptor

Parameters

ntypes: Number of atom types.
reuse: The weights in the networks should be reused when get the variable.
suffix: Name suffix to identify this descriptor

Returns

embedded_types: The computational graph for embedded types

init_variables(graph: tensorflow.python.framework.ops.Graph, graph_def: tensorflow.core.framework.graph_pb2.GraphDef, suffix='') → None[source]

Init the type embedding net variables with the given dict

Parameters

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
suffix: Name suffix to identify this descriptor

deepmd.utils.type_embed.embed_atom_type(ntypes: int, natoms: tensorflow.python.framework.ops.Tensor, type_embedding: tensorflow.python.framework.ops.Tensor)[source]

Make the embedded type for the atoms in system. The atoms are assumed to be sorted according to the type, thus their types are described by a tf.Tensor natoms, see explanation below.

Parameters

ntypes:: Number of types.
natoms:: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
type_embedding:: The type embedding. It has the shape of [ntypes, embedding_dim]

Returns

atom_embedding: The embedded type of each atom. It has the shape of [numb_atoms, embedding_dim]

deepmd.utils.weight_avg module

deepmd.utils.weight_avg.weighted_average(errors: List[Dict[str, Tuple[float, float]]]) → Dict[source]

Compute wighted average of prediction errors for model.

Parameters

errorsList[Dict[str, Tuple[float, float]]]: List: the error of systems Dict: the error of quantities, name given by the key Tuple: (error, weight)

Returns

Dict: weighted averages

Submodules

deepmd.calculator module

ASE calculator interface module.

class deepmd.calculator.DP(model: Union[str, pathlib.Path], label: str = 'DP', type_dict: Optional[Dict[str, int]] = None, **kwargs)[source]

Bases: ase.calculators.calculator.Calculator

Implementation of ASE deepmd calculator.

Implemented propertie are energy, forces and stress

Parameters

modelUnion[str, Path]: path to the model
labelstr, optional: calculator label, by default “DP”
type_dictDict[str, int], optional: mapping of element types and their numbers, best left None and the calculator will infer this information from model, by default None

Examples

Compute potential energy

>>> from ase import Atoms
>>> from deepmd.calculator import DP
>>> water = Atoms('H2O',
>>>             positions=[(0.7601, 1.9270, 1),
>>>                        (1.9575, 1, 1),
>>>                        (1., 1., 1.)],
>>>             cell=[100, 100, 100],
>>>             calculator=DP(model="frozen_model.pb"))
>>> print(water.get_potential_energy())
>>> print(water.get_forces())

Run BFGS structure optimization

>>> from ase.optimize import BFGS
>>> dyn = BFGS(water)
>>> dyn.run(fmax=1e-6)
>>> print(water.get_positions())

Attributes

directory
label

Methods

`band_structure`()	Create band-structure object for plotting.
`calculate`([atoms, properties, system_changes])	Run calculation with deepmd model.
`calculate_numerical_forces`(atoms[, d])	Calculate numerical forces using finite difference.
`calculate_numerical_stress`(atoms[, d, voigt])	Calculate numerical stress using finite difference.
`calculate_properties`(atoms, properties)	This method is experimental; currently for internal use.
`check_state`(atoms[, tol])	Check for any system changes since last calculation.
`get_magnetic_moments`([atoms])	Calculate magnetic moments projected onto atoms.
`get_property`(name[, atoms, allow_calculation])	Get the named property.
`get_stresses`([atoms])	the calculator should return intensive stresses, i.e., such that stresses.sum(axis=0) == stress
`read`(label)	Read atoms, parameters and calculated properties from output file.
`reset`()	Clear all information from old calculation.
`set`(**kwargs)	Set parameters like set(key1=value1, key2=value2, ...).
`set_label`(label)	Set label and convert label to directory and prefix.

calculation_required
export_properties
get_atoms
get_charges
get_default_parameters
get_dipole_moment
get_forces
get_magnetic_moment
get_potential_energies
get_potential_energy
get_stress
read_atoms
todict

calculate(atoms: Optional[Atoms] = None, properties: List[str] = ['energy', 'forces', 'virial'], system_changes: List[str] = ['positions', 'numbers', 'cell', 'pbc', 'initial_charges', 'initial_magmoms'])[source]

Run calculation with deepmd model.

Parameters

atomsOptional[Atoms], optional: atoms object to run the calculation on, by default None
propertiesList[str], optional: unused, only for function signature compatibility, by default [“energy”, “forces”, “stress”]
system_changesList[str], optional: unused, only for function signature compatibility, by default all_changes

implemented_properties: List[str] = ['energy', 'free_energy', 'forces', 'virial', 'stress']: Properties calculator can handle (energy, forces, …)

name = 'DP'

deepmd.common module

Collection of functions and classes used throughout the whole package.

class deepmd.common.ClassArg[source]

Bases: object

Class that take care of input json/yaml parsing.

The rules for parsing are defined by the add method, than parse is called to process the supplied dict

Attributes

arg_dict: Dict[str, Any]: dictionary containing parsing rules
alias_map: Dict[str, Any]: dictionary with keyword aliases

Methods

`add`(key, types_[, alias, default, must])	Add key to be parsed.
`get_dict`()	Get dictionary built from rules defined by add method.
`parse`(jdata)	Parse input dictionary, use the rules defined by add method.

add(key: str, types_: Union[type, List[type]], alias: Optional[Union[str, List[str]]] = None, default: Optional[Any] = None, must: bool = False) → deepmd.common.ClassArg[source]

Add key to be parsed.

Parameters

keystr: key name
types_Union[type, List[type]]: list of allowed key types
aliasOptional[Union[str, List[str]]], optional: alias for the key, by default None
defaultAny, optional: default value for the key, by default None
mustbool, optional: if the key is mandatory, by default False

Returns

ClassArg: instance with added key

get_dict() → Dict[str, Any][source]

Get dictionary built from rules defined by add method.

Returns

Dict[str, Any]: settings dictionary with default values

parse(jdata: Dict[str, Any]) → Dict[str, Any][source]

Parse input dictionary, use the rules defined by add method.

Parameters

jdataDict[str, Any]: loaded json/yaml data

Returns

Dict[str, Any]: parsed dictionary

deepmd.common.add_data_requirement(key: str, ndof: int, atomic: bool = False, must: bool = False, high_prec: bool = False, type_sel: Optional[bool] = None, repeat: int = 1, default: float = 0.0)[source]

Specify data requirements for training.

Parameters

keystr: type of data stored in corresponding *.npy file e.g. forces or energy
ndofint: number of the degrees of freedom, this is tied to atomic parameter e.g. forces have atomic=True and ndof=3
atomicbool, optional: specifies whwther the ndof keyworrd applies to per atom quantity or not, by default False
mustbool, optional: specifi if the *.npy data file must exist, by default False
high_precbool, optional: if tru load data to np.float64 else np.float32, by default False
type_selbool, optional: select only certain type of atoms, by default None
repeatint, optional: if specify repaeat data repeat times, by default 1
defaultfloat, optional, default=0.: default value of data

deepmd.common.cast_precision(func: Callable) → Callable[source]

A decorator that casts and casts back the input and output tensor of a method.

The decorator should be used in a classmethod.

The decorator will do the following thing: (1) It casts input Tensors from GLOBAL_TF_FLOAT_PRECISION to precision defined by property precision. (2) It casts output Tensors from precision to GLOBAL_TF_FLOAT_PRECISION. (3) It checks inputs and outputs and only casts when input or output is a Tensor and its dtype matches GLOBAL_TF_FLOAT_PRECISION and precision, respectively. If it does not match (e.g. it is an integer), the decorator will do nothing on it.

Returns

Callable: a decorator that casts and casts back the input and output tensor of a method

Examples

>>> class A:
...   @property
...   def precision(self):
...     return tf.float32
...
...   @cast_precision
...   def f(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
...     return x ** 2 + y

deepmd.common.expand_sys_str(root_dir: Union[str, pathlib.Path]) → List[str][source]

Recursively iterate over directories taking those that contain type.raw file.

Parameters

root_dirUnion[str, Path]: starting directory

Returns

List[str]: list of string pointing to system directories

deepmd.common.gelu(x: tensorflow.python.framework.ops.Tensor) → tensorflow.python.framework.ops.Tensor[source]

Gaussian Error Linear Unit.

This is a smoother version of the RELU, implemented by custom operator.

Parameters

xtf.Tensor: float Tensor to perform activation

Returns

tf.Tensor: x with the GELU activation applied

References

Original paper https://arxiv.org/abs/1606.08415

deepmd.common.gelu_tf(x: tensorflow.python.framework.ops.Tensor) → tensorflow.python.framework.ops.Tensor[source]

Gaussian Error Linear Unit.

This is a smoother version of the RELU, implemented by TF.

Parameters

xtf.Tensor: float Tensor to perform activation

Returns

tf.Tensor: x with the GELU activation applied

References

Original paper https://arxiv.org/abs/1606.08415

deepmd.common.get_activation_func(activation_fn: Optional[_ACTIVATION]) → Optional[Callable[[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor]][source]

Get activation function callable based on string name.

Parameters

activation_fn_ACTIVATION: one of the defined activation functions

Returns

Callable[[tf.Tensor], tf.Tensor]: correspondingg TF callable

Raises

RuntimeError: if unknown activation function is specified

deepmd.common.get_np_precision(precision: _PRECISION) → numpy.dtype[source]

Get numpy precision constant from string.

Parameters

precision_PRECISION: string name of numpy constant or default

Returns

np.dtype: numpy presicion constant

Raises

RuntimeError: if string is invalid

deepmd.common.get_precision(precision: _PRECISION) → Any[source]

Convert str to TF DType constant.

Parameters

precision_PRECISION: one of the allowed precisions

Returns

tf.python.framework.dtypes.DType: appropriate TF constant

Raises

RuntimeError: if supplied precision string does not have acorresponding TF constant

deepmd.common.j_loader(filename: Union[str, pathlib.Path]) → Dict[str, Any][source]

Load yaml or json settings file.

Parameters

filenameUnion[str, Path]: path to file

Returns

Dict[str, Any]: loaded dictionary

Raises

TypeError: if the supplied file is of unsupported type

deepmd.common.j_must_have(jdata: Dict[str, _DICT_VAL], key: str, deprecated_key: List[str] = []) → _DICT_VAL[source]

Assert that supplied dictionary conaines specified key.

Returns

_DICT_VAL: value that was store unde supplied key

Raises

RuntimeError: if the key is not present

deepmd.common.make_default_mesh(test_box: numpy.ndarray, cell_size: float = 3.0) → numpy.ndarray[source]

Get number of cells of size=`cell_size` fit into average box.

Parameters

test_boxnp.ndarray: numpy array with cells of shape Nx9
cell_sizefloat, optional: length of one cell, by default 3.0

Returns

np.ndarray: mesh for supplied boxes, how many cells fit in each direction

deepmd.common.safe_cast_tensor(input: tensorflow.python.framework.ops.Tensor, from_precision: tensorflow.python.framework.dtypes.DType, to_precision: tensorflow.python.framework.dtypes.DType) → tensorflow.python.framework.ops.Tensor[source]

Convert a Tensor from a precision to another precision.

If input is not a Tensor or without the specific precision, the method will not cast it.

Parameters

input: tf.Tensor: input tensor
precisiontf.DType: Tensor data type that casts to

Returns

tf.Tensor: casted Tensor

deepmd.common.select_idx_map(atom_types: numpy.ndarray, select_types: numpy.ndarray) → numpy.ndarray[source]

Build map of indices for element supplied element types from all atoms list.

Parameters

atom_typesnp.ndarray: array specifing type for each atoms as integer
select_typesnp.ndarray: types of atoms you want to find indices for

Returns

np.ndarray: indices of types of atoms defined by select_types in atom_types array

Warning

select_types array will be sorted before finding indices in atom_types

deepmd.env module

Module that sets tensorflow working environment and exports inportant constants.

deepmd.env.GLOBAL_ENER_FLOAT_PRECISION: alias of numpy.float64

deepmd.env.GLOBAL_NP_FLOAT_PRECISION: alias of numpy.float64

deepmd.env.global_cvt_2_ener_float(xx: tensorflow.python.framework.ops.Tensor) → tensorflow.python.framework.ops.Tensor[source]

Cast tensor to globally set energy precision.

Parameters

xxtf.Tensor: input tensor

Returns

tf.Tensor: output tensor cast to GLOBAL_ENER_FLOAT_PRECISION

deepmd.env.global_cvt_2_tf_float(xx: tensorflow.python.framework.ops.Tensor) → tensorflow.python.framework.ops.Tensor[source]

Cast tensor to globally set TF precision.

Parameters

xxtf.Tensor: input tensor

Returns

tf.Tensor: output tensor cast to GLOBAL_TF_FLOAT_PRECISION

deepmd.env.reset_default_tf_session_config(cpu_only: bool)[source]

Limit tensorflow session to CPU or not.

Parameters

cpu_onlybool: If enabled, no GPU device is visible to the TensorFlow Session.

OP API

op_module

Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.

deepmd.env.op_module.Descrpt(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, sel_a, sel_r, axis_rule, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
sel_a – A list of ints.
sel_r – A list of ints.
axis_rule – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist, axis, rot_mat).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32. axis: A Tensor of type int32. rot_mat: A Tensor. Has the same type as coord.

deepmd.env.op_module.DescrptNorot(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.DescrptSeA(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.DescrptSeAEf(coord, type, natoms, box, mesh, ef, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
ef – A Tensor. Must have the same type as coord.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.DescrptSeAEfPara(coord, type, natoms, box, mesh, ef, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
ef – A Tensor. Must have the same type as coord.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.DescrptSeAEfVert(coord, type, natoms, box, mesh, ef, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
ef – A Tensor. Must have the same type as coord.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.DescrptSeR(coord, type, natoms, box, mesh, davg, dstd, rcut, rcut_smth, sel, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut – A float.
rcut_smth – A float.
sel – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.EwaldRecp(coord, charge, natoms, box, ewald_beta, ewald_h, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
charge – A Tensor. Must have the same type as coord.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
ewald_beta – A float.
ewald_h – A float.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (energy, force, virial).

energy: A Tensor. Has the same type as coord. force: A Tensor. Has the same type as coord. virial: A Tensor. Has the same type as coord.

deepmd.env.op_module.Gelu(x, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.GeluCustom(x, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.GeluGrad(dy, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.GeluGradCustom(dy, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.GeluGradGrad(dy, dy_, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
dy – A Tensor. Must have the same type as dy.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.GeluGradGradCustom(dy, dy_, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
dy – A Tensor. Must have the same type as dy.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.MapAparam(aparam, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

aparam – A Tensor. Must be one of the following types: float32, float64.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as aparam.

deepmd.env.op_module.MapNvnmd(x, v, dv, grad_v, grad_dv, prec, nbit, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
v – A Tensor. Must have the same type as x.
dv – A Tensor. Must have the same type as x.
grad_v – A Tensor. Must have the same type as x.
grad_dv – A Tensor. Must have the same type as x.
prec – A float.
nbit – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.MatmulNvnmd(x, w, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as x.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.NeighborStat(coord, type, natoms, box, mesh, rcut, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
rcut – A float.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (max_nbor_size, min_nbor_dist).

max_nbor_size: A Tensor of type int32. min_nbor_dist: A Tensor. Has the same type as coord.

deepmd.env.op_module.PairTab(table_info, table_data, type, rij, nlist, natoms, scale, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

table_info – A Tensor of type float64.
table_data – A Tensor of type float64.
type – A Tensor of type int32.
rij – A Tensor. Must be one of the following types: float32, float64.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
scale – A Tensor. Must have the same type as rij.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (atom_energy, force, atom_virial).

atom_energy: A Tensor. Has the same type as rij. force: A Tensor. Has the same type as rij. atom_virial: A Tensor. Has the same type as rij.

deepmd.env.op_module.ParallelProdForceSeA(net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, parallel=False, start_frac=0, end_frac=1, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
parallel – An optional bool. Defaults to False.
start_frac – An optional float. Defaults to 0.
end_frac – An optional float. Defaults to 1.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdEnvMatA(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

Compute the environment matrix for descriptor se_e2_a.

Each row of the environment matrix $\mathcal{R}^i$ can be constructed as follows

\[(\mathcal{R}^i)_j = [ \begin{array}{c} s(r_{ji}) & \frac{s(r_{ji})x_{ji}}{r_{ji}} & \frac{s(r_{ji})y_{ji}}{r_{ji}} & \frac{s(r_{ji})z_{ji}}{r_{ji}} \end{array} ]\]

In the above equation, $\mathbf{R}_{ji}=\mathbf{R}_j-\mathbf{R}_i = (x_{ji}, y_{ji}, z_{ji})$ is the relative coordinate and $r_{ji}=\lVert \mathbf{R}_{ji} \lVert$ is its norm. The switching function $s(r)$ is defined as:

\[\begin{split}s(r)= \begin{cases} \frac{1}{r}, & r<r_s \\ \frac{1}{r} \{ {(\frac{r - r_s}{ r_c - r_s})}^3 (-6 {(\frac{r - r_s}{ r_c - r_s})}^2 +15 \frac{r - r_s}{ r_c - r_s} -10) +1 \}, & r_s \leq r<r_c \\ 0, & r \geq r_c \end{cases}\end{split}\]

Note that the environment matrix is normalized by davg and dstd.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64. The coordinates of atoms.
type – A Tensor of type int32. The types of atoms.
natoms – A Tensor of type int32. The number of atoms. This tensor has the length of Ntypes + 2. natoms[0]: number of local atoms. natoms[1]: total number of atoms held by this processor. natoms[i]: 2 <= i < Ntypes+2, number of type i atoms.
box – A Tensor. Must have the same type as coord. The box of frames.
mesh – A Tensor of type int32. Gor historical reasons, only the length of the Tensor matters. If size of mesh == 6, pbc is assumed. If size of mesh == 0, no-pbc is assumed.
davg – A Tensor. Must have the same type as coord. Average value of the environment matrix for normalization.
dstd – A Tensor. Must have the same type as coord. Standard deviation of the environment matrix for normalization.
rcut_a – A float. This argument is not used.
rcut_r – A float. The cutoff radius for the environment matrix.
rcut_r_smth – A float. From where the environment matrix should be smoothed.
sel_a – A list of ints. sel_a[i] specifies the maxmum number of type i atoms in the cut-off radius.
sel_r – A list of ints. This argument is not used.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. The environment matrix. descrpt_deriv: A Tensor. Has the same type as coord. The derivative of the environment matrix. rij: A Tensor. Has the same type as coord. The distance between the atoms. nlist: A Tensor of type int32. The neighbor list of each atom.

deepmd.env.op_module.ProdEnvMatAMix(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

Compute the environment matrix mixing the atom types.

The sorting of neighbor atoms depends not on atom types, but on the distance and index. The atoms in nlist matrix will gather forward and thus save space for gaps of types in ProdEnvMatA, resulting in optimized and relative small sel_a.

The additional outputs are listed as following:

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist, ntype, nmask).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32. ntype: A Tensor of type int32. The corresponding atom types in nlist. nmask: A Tensor of type bool. The atom mask in nlist.

deepmd.env.op_module.ProdEnvMatANvnmdQuantize(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.ProdEnvMatR(coord, type, natoms, box, mesh, davg, dstd, rcut, rcut_smth, sel, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut – A float.
rcut_smth – A float.
sel – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.ProdForce(net_deriv, in_deriv, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdForceNorot(net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdForceSeA(net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdForceSeR(net_deriv, in_deriv, nlist, natoms, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdVirial(net_deriv, in_deriv, rij, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdVirialNorot(net_deriv, in_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdVirialSeA(net_deriv, in_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.ProdVirialSeR(net_deriv, in_deriv, rij, nlist, natoms, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.QuantizeNvnmd(x, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.SoftMinForce(du, sw_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

du – A Tensor. Must be one of the following types: float32, float64.
sw_deriv – A Tensor. Must have the same type as du.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as du.

deepmd.env.op_module.SoftMinSwitch(type, rij, nlist, natoms, sel_a, sel_r, alpha, rmin, rmax, name=None)

TODO: add doc.

Parameters

type – A Tensor of type int32.
rij – A Tensor. Must be one of the following types: float32, float64.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
sel_a – A list of ints.
sel_r – A list of ints.
alpha – A float.
rmin – A float.
rmax – A float.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (sw_value, sw_deriv).

sw_value: A Tensor. Has the same type as rij. sw_deriv: A Tensor. Has the same type as rij.

deepmd.env.op_module.SoftMinVirial(du, sw_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

du – A Tensor. Must be one of the following types: float32, float64.
sw_deriv – A Tensor. Must have the same type as du.
rij – A Tensor. Must have the same type as du.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as du. atom_virial: A Tensor. Has the same type as du.

deepmd.env.op_module.TabulateFusion(table, table_info, em_x, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionGrad(table, table_info, em_x, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (dy_dem_x, dy_dem).

dy_dem_x: A Tensor. Has the same type as table. dy_dem: A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionGradGrad(table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem_x – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeA(table, table_info, em_x, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeAGrad(table, table_info, em_x, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (dy_dem_x, dy_dem).

dy_dem_x: A Tensor. Has the same type as table. dy_dem: A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeAGradGrad(table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem_x – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeR(table, table_info, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeRGrad(table, table_info, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeRGradGrad(table, table_info, em, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeT(table, table_info, em_x, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeTGrad(table, table_info, em_x, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (dy_dem_x, dy_dem).

dy_dem_x: A Tensor. Has the same type as table. dy_dem: A Tensor. Has the same type as table.

deepmd.env.op_module.TabulateFusionSeTGradGrad(table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem_x – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.Tanh2Nvnmd(x, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.Tanh4Nvnmd(x, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.UnaggregatedDy2Dx(z, w, dy_dx, dy2_dx, ybar, functype, name=None)

TODO: add doc.

Parameters

z – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as z.
dy_dx – A Tensor. Must have the same type as z.
dy2_dx – A Tensor. Must have the same type as z.
ybar – A Tensor. Must have the same type as z.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as z.

deepmd.env.op_module.UnaggregatedDy2DxS(y, dy, w, xbar, functype, name=None)

TODO: add doc.

Parameters

y – A Tensor. Must be one of the following types: float32, float64.
dy – A Tensor. Must have the same type as y.
w – A Tensor. Must have the same type as y.
xbar – A Tensor. Must have the same type as y.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as y.

deepmd.env.op_module.UnaggregatedDyDx(z, w, dy_dx, ybar, functype, name=None)

TODO: add doc.

Parameters

z – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as z.
dy_dx – A Tensor. Must have the same type as z.
ybar – A Tensor. Must have the same type as z.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as z.

deepmd.env.op_module.UnaggregatedDyDxS(y, w, xbar, functype, name=None)

TODO: add doc.

Parameters

y – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as y.
xbar – A Tensor. Must have the same type as y.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as y.

deepmd.env.op_module.descrpt(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, sel_a, sel_r, axis_rule, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
sel_a – A list of ints.
sel_r – A list of ints.
axis_rule – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist, axis, rot_mat).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32. axis: A Tensor of type int32. rot_mat: A Tensor. Has the same type as coord.

deepmd.env.op_module.descrpt_norot(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.descrpt_se_a(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.descrpt_se_a_ef(coord, type, natoms, box, mesh, ef, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
ef – A Tensor. Must have the same type as coord.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.descrpt_se_a_ef_para(coord, type, natoms, box, mesh, ef, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
ef – A Tensor. Must have the same type as coord.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.descrpt_se_a_ef_vert(coord, type, natoms, box, mesh, ef, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
ef – A Tensor. Must have the same type as coord.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.descrpt_se_r(coord, type, natoms, box, mesh, davg, dstd, rcut, rcut_smth, sel, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut – A float.
rcut_smth – A float.
sel – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.ewald_recp(coord, charge, natoms, box, ewald_beta, ewald_h, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
charge – A Tensor. Must have the same type as coord.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
ewald_beta – A float.
ewald_h – A float.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (energy, force, virial).

energy: A Tensor. Has the same type as coord. force: A Tensor. Has the same type as coord. virial: A Tensor. Has the same type as coord.

deepmd.env.op_module.gelu(x, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.gelu_custom(x, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.gelu_grad(dy, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.gelu_grad_custom(dy, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.gelu_grad_grad(dy, dy_, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
dy – A Tensor. Must have the same type as dy.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.gelu_grad_grad_custom(dy, dy_, x, name=None)

TODO: add doc.

Parameters

dy – A Tensor. Must be one of the following types: float32, float64.
dy – A Tensor. Must have the same type as dy.
x – A Tensor. Must have the same type as dy.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as dy.

deepmd.env.op_module.map_aparam(aparam, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

aparam – A Tensor. Must be one of the following types: float32, float64.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as aparam.

deepmd.env.op_module.map_nvnmd(x, v, dv, grad_v, grad_dv, prec, nbit, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
v – A Tensor. Must have the same type as x.
dv – A Tensor. Must have the same type as x.
grad_v – A Tensor. Must have the same type as x.
grad_dv – A Tensor. Must have the same type as x.
prec – A float.
nbit – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.matmul_nvnmd(x, w, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as x.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.neighbor_stat(coord, type, natoms, box, mesh, rcut, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
rcut – A float.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (max_nbor_size, min_nbor_dist).

max_nbor_size: A Tensor of type int32. min_nbor_dist: A Tensor. Has the same type as coord.

deepmd.env.op_module.pair_tab(table_info, table_data, type, rij, nlist, natoms, scale, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

table_info – A Tensor of type float64.
table_data – A Tensor of type float64.
type – A Tensor of type int32.
rij – A Tensor. Must be one of the following types: float32, float64.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
scale – A Tensor. Must have the same type as rij.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (atom_energy, force, atom_virial).

atom_energy: A Tensor. Has the same type as rij. force: A Tensor. Has the same type as rij. atom_virial: A Tensor. Has the same type as rij.

deepmd.env.op_module.parallel_prod_force_se_a(net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, parallel=False, start_frac=0, end_frac=1, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
parallel – An optional bool. Defaults to False.
start_frac – An optional float. Defaults to 0.
end_frac – An optional float. Defaults to 1.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_env_mat_a(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

Compute the environment matrix for descriptor se_e2_a.

Each row of the environment matrix $\mathcal{R}^i$ can be constructed as follows

\[(\mathcal{R}^i)_j = [ \begin{array}{c} s(r_{ji}) & \frac{s(r_{ji})x_{ji}}{r_{ji}} & \frac{s(r_{ji})y_{ji}}{r_{ji}} & \frac{s(r_{ji})z_{ji}}{r_{ji}} \end{array} ]\]

In the above equation, $\mathbf{R}_{ji}=\mathbf{R}_j-\mathbf{R}_i = (x_{ji}, y_{ji}, z_{ji})$ is the relative coordinate and $r_{ji}=\lVert \mathbf{R}_{ji} \lVert$ is its norm. The switching function $s(r)$ is defined as:

\[\begin{split}s(r)= \begin{cases} \frac{1}{r}, & r<r_s \\ \frac{1}{r} \{ {(\frac{r - r_s}{ r_c - r_s})}^3 (-6 {(\frac{r - r_s}{ r_c - r_s})}^2 +15 \frac{r - r_s}{ r_c - r_s} -10) +1 \}, & r_s \leq r<r_c \\ 0, & r \geq r_c \end{cases}\end{split}\]

Note that the environment matrix is normalized by davg and dstd.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64. The coordinates of atoms.
type – A Tensor of type int32. The types of atoms.
natoms – A Tensor of type int32. The number of atoms. This tensor has the length of Ntypes + 2. natoms[0]: number of local atoms. natoms[1]: total number of atoms held by this processor. natoms[i]: 2 <= i < Ntypes+2, number of type i atoms.
box – A Tensor. Must have the same type as coord. The box of frames.
mesh – A Tensor of type int32. Gor historical reasons, only the length of the Tensor matters. If size of mesh == 6, pbc is assumed. If size of mesh == 0, no-pbc is assumed.
davg – A Tensor. Must have the same type as coord. Average value of the environment matrix for normalization.
dstd – A Tensor. Must have the same type as coord. Standard deviation of the environment matrix for normalization.
rcut_a – A float. This argument is not used.
rcut_r – A float. The cutoff radius for the environment matrix.
rcut_r_smth – A float. From where the environment matrix should be smoothed.
sel_a – A list of ints. sel_a[i] specifies the maxmum number of type i atoms in the cut-off radius.
sel_r – A list of ints. This argument is not used.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. The environment matrix. descrpt_deriv: A Tensor. Has the same type as coord. The derivative of the environment matrix. rij: A Tensor. Has the same type as coord. The distance between the atoms. nlist: A Tensor of type int32. The neighbor list of each atom.

deepmd.env.op_module.prod_env_mat_a_mix(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

Compute the environment matrix mixing the atom types.

The sorting of neighbor atoms depends not on atom types, but on the distance and index. The atoms in nlist matrix will gather forward and thus save space for gaps of types in ProdEnvMatA, resulting in optimized and relative small sel_a.

The additional outputs are listed as following:

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist, ntype, nmask).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32. ntype: A Tensor of type int32. The corresponding atom types in nlist. nmask: A Tensor of type bool. The atom mask in nlist.

deepmd.env.op_module.prod_env_mat_a_nvnmd_quantize(coord, type, natoms, box, mesh, davg, dstd, rcut_a, rcut_r, rcut_r_smth, sel_a, sel_r, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut_a – A float.
rcut_r – A float.
rcut_r_smth – A float.
sel_a – A list of ints.
sel_r – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.prod_env_mat_r(coord, type, natoms, box, mesh, davg, dstd, rcut, rcut_smth, sel, name=None)

TODO: add doc.

Parameters

coord – A Tensor. Must be one of the following types: float32, float64.
type – A Tensor of type int32.
natoms – A Tensor of type int32.
box – A Tensor. Must have the same type as coord.
mesh – A Tensor of type int32.
davg – A Tensor. Must have the same type as coord.
dstd – A Tensor. Must have the same type as coord.
rcut – A float.
rcut_smth – A float.
sel – A list of ints.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (descrpt, descrpt_deriv, rij, nlist).

descrpt: A Tensor. Has the same type as coord. descrpt_deriv: A Tensor. Has the same type as coord. rij: A Tensor. Has the same type as coord. nlist: A Tensor of type int32.

deepmd.env.op_module.prod_force(net_deriv, in_deriv, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_force_norot(net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_force_se_a(net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_force_se_r(net_deriv, in_deriv, nlist, natoms, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_virial(net_deriv, in_deriv, rij, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_virial_norot(net_deriv, in_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_virial_se_a(net_deriv, in_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.prod_virial_se_r(net_deriv, in_deriv, rij, nlist, natoms, name=None)

TODO: add doc.

Parameters

net_deriv – A Tensor. Must be one of the following types: float32, float64.
in_deriv – A Tensor. Must have the same type as net_deriv.
rij – A Tensor. Must have the same type as net_deriv.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as net_deriv. atom_virial: A Tensor. Has the same type as net_deriv.

deepmd.env.op_module.quantize_nvnmd(x, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.soft_min_force(du, sw_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

du – A Tensor. Must be one of the following types: float32, float64.
sw_deriv – A Tensor. Must have the same type as du.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as du.

deepmd.env.op_module.soft_min_switch(type, rij, nlist, natoms, sel_a, sel_r, alpha, rmin, rmax, name=None)

TODO: add doc.

Parameters

type – A Tensor of type int32.
rij – A Tensor. Must be one of the following types: float32, float64.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
sel_a – A list of ints.
sel_r – A list of ints.
alpha – A float.
rmin – A float.
rmax – A float.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (sw_value, sw_deriv).

sw_value: A Tensor. Has the same type as rij. sw_deriv: A Tensor. Has the same type as rij.

deepmd.env.op_module.soft_min_virial(du, sw_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

du – A Tensor. Must be one of the following types: float32, float64.
sw_deriv – A Tensor. Must have the same type as du.
rij – A Tensor. Must have the same type as du.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (virial, atom_virial).

virial: A Tensor. Has the same type as du. atom_virial: A Tensor. Has the same type as du.

deepmd.env.op_module.tabulate_fusion(table, table_info, em_x, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_grad(table, table_info, em_x, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (dy_dem_x, dy_dem).

dy_dem_x: A Tensor. Has the same type as table. dy_dem: A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_grad_grad(table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem_x – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_a(table, table_info, em_x, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_a_grad(table, table_info, em_x, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (dy_dem_x, dy_dem).

dy_dem_x: A Tensor. Has the same type as table. dy_dem: A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_a_grad_grad(table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem_x – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_r(table, table_info, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_r_grad(table, table_info, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_r_grad_grad(table, table_info, em, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_t(table, table_info, em_x, em, last_layer_size, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
last_layer_size – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_t_grad(table, table_info, em_x, em, dy, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dy – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A tuple of Tensor objects (dy_dem_x, dy_dem).

dy_dem_x: A Tensor. Has the same type as table. dy_dem: A Tensor. Has the same type as table.

deepmd.env.op_module.tabulate_fusion_se_t_grad_grad(table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, descriptor, name=None)

TODO: add doc.

Parameters

table – A Tensor. Must be one of the following types: float32, float64.
table_info – A Tensor. Must have the same type as table.
em_x – A Tensor. Must have the same type as table.
em – A Tensor. Must have the same type as table.
dz_dy_dem_x – A Tensor. Must have the same type as table.
dz_dy_dem – A Tensor. Must have the same type as table.
descriptor – A Tensor. Must have the same type as table.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as table.

deepmd.env.op_module.tanh2_nvnmd(x, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.tanh4_nvnmd(x, isround, nbit1, nbit2, nbit3, name=None)

TODO: add doc.

Parameters

x – A Tensor. Must be one of the following types: float32, float64.
isround – An int.
nbit1 – An int.
nbit2 – An int.
nbit3 – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as x.

deepmd.env.op_module.unaggregated_dy2_dx(z, w, dy_dx, dy2_dx, ybar, functype, name=None)

TODO: add doc.

Parameters

z – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as z.
dy_dx – A Tensor. Must have the same type as z.
dy2_dx – A Tensor. Must have the same type as z.
ybar – A Tensor. Must have the same type as z.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as z.

deepmd.env.op_module.unaggregated_dy2_dx_s(y, dy, w, xbar, functype, name=None)

TODO: add doc.

Parameters

y – A Tensor. Must be one of the following types: float32, float64.
dy – A Tensor. Must have the same type as y.
w – A Tensor. Must have the same type as y.
xbar – A Tensor. Must have the same type as y.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as y.

deepmd.env.op_module.unaggregated_dy_dx(z, w, dy_dx, ybar, functype, name=None)

TODO: add doc.

Parameters

z – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as z.
dy_dx – A Tensor. Must have the same type as z.
ybar – A Tensor. Must have the same type as z.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as z.

deepmd.env.op_module.unaggregated_dy_dx_s(y, w, xbar, functype, name=None)

TODO: add doc.

Parameters

y – A Tensor. Must be one of the following types: float32, float64.
w – A Tensor. Must have the same type as y.
xbar – A Tensor. Must have the same type as y.
functype – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as y.

op_grads_module

Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.

deepmd.env.op_grads_module.ProdForceGrad(grad, net_deriv, in_deriv, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.ProdForceSeAGrad(grad, net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.ProdForceSeRGrad(grad, net_deriv, in_deriv, nlist, natoms, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.ProdVirialGrad(grad, net_deriv, in_deriv, rij, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.ProdVirialSeAGrad(grad, net_deriv, in_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.ProdVirialSeRGrad(grad, net_deriv, in_deriv, rij, nlist, natoms, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.SoftMinForceGrad(grad, du, sw_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
du – A Tensor. Must have the same type as grad.
sw_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.SoftMinVirialGrad(grad, du, sw_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
du – A Tensor. Must have the same type as grad.
sw_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.prod_force_grad(grad, net_deriv, in_deriv, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.prod_force_se_a_grad(grad, net_deriv, in_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.prod_force_se_r_grad(grad, net_deriv, in_deriv, nlist, natoms, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.prod_virial_grad(grad, net_deriv, in_deriv, rij, nlist, axis, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
axis – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.prod_virial_se_a_grad(grad, net_deriv, in_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.prod_virial_se_r_grad(grad, net_deriv, in_deriv, rij, nlist, natoms, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
net_deriv – A Tensor. Must have the same type as grad.
in_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.soft_min_force_grad(grad, du, sw_deriv, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
du – A Tensor. Must have the same type as grad.
sw_deriv – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

deepmd.env.op_grads_module.soft_min_virial_grad(grad, du, sw_deriv, rij, nlist, natoms, n_a_sel, n_r_sel, name=None)

TODO: add doc.

Parameters

grad – A Tensor. Must be one of the following types: float32, float64.
du – A Tensor. Must have the same type as grad.
sw_deriv – A Tensor. Must have the same type as grad.
rij – A Tensor. Must have the same type as grad.
nlist – A Tensor of type int32.
natoms – A Tensor of type int32.
n_a_sel – An int.
n_r_sel – An int.
name – A name for the operation (optional).

Returns

A Tensor. Has the same type as grad.

C++ API

Class Hierarchy

Namespace deepmd

File Hierarchy

Directory source
- Directory api_cc
  
  Directory include
  
  File AtomMap.h
  
  File common.h
  
  File DataModifier.h
  
  File DeepPot.h
  
  File DeepTensor.h
  
  File tf_private.h
  
  File tf_public.h

Full API

Namespaces

Namespace deepmd

Contents

Classes
Functions
Typedefs

Classes

Functions

Typedefs

Namespace tensorflow

Classes and Structs

Struct deepmd_exception

Defined in File common.h

Inheritance Relationships

Derived Type

public deepmd::tf_exception (Struct tf_exception)

Struct Documentation

struct deepmd_exception: Subclassed by deepmd::tf_exception

Struct NeighborListData

Defined in File common.h

Struct Documentation

struct NeighborListData

Public Functions

void copy_from_nlist(const InputNlist &inlist)

void shuffle(const std::vector<int> &fwd_map)

void shuffle(const deepmd::AtomMap<VALUETYPE> &map)

void shuffle_exclude_empty(const std::vector<int> &fwd_map)

void make_inlist(InputNlist &inlist)

Public Members

std::vector<int> ilist: Array stores the core region atom’s index.

std::vector<std::vector<int>> jlist: Array stores the core region atom’s neighbor index.

std::vector<int> numneigh: Array stores the number of neighbors of core region atoms.

std::vector<int*> firstneigh: Array stores the the location of the first neighbor of core region atoms.

Struct tf_exception

Defined in File common.h

Inheritance Relationships

Base Type

public deepmd_exception (Struct deepmd_exception)

Struct Documentation

struct tf_exception : public deepmd_exception 

Throw exception if TensorFlow doesn’t work.

Public Functions

inline tf_exception()

inline tf_exception(const std::string &msg)

Template Class AtomMap

Defined in File AtomMap.h

Class Documentation

template<typename VALUETYPE> class AtomMap

Public Functions

AtomMap()

AtomMap(const std::vector<int>::const_iterator in_begin, const std::vector<int>::const_iterator in_end)

void forward(typename std::vector<VALUETYPE>::iterator out, const typename std::vector<VALUETYPE>::const_iterator in, const int stride = 1) const

void backward(typename std::vector<VALUETYPE>::iterator out, const typename std::vector<VALUETYPE>::const_iterator in, const int stride = 1) const

inline const std::vector<int> &get_type() const

inline const std::vector<int> &get_fwd_map() const

inline const std::vector<int> &get_bkw_map() const

Class DeepPot

Defined in File DeepPot.h

Class Documentation

class DeepPot

Deep Potential.

Public Functions

DeepPot(): DP constructor without initialization.

~DeepPot()

DeepPot(const std::string &model, const int &gpu_rank = 0, const std::string &file_content = "")

DP constructor with initialization.

Parameters

model – [in] The name of the frozen model file.
gpu_rank – [in] The GPU rank. Default is 0.
file_content – [in] The content of the model file. If it is not empty, DP will read from the string instead of the file.

void init(const std::string &model, const int &gpu_rank = 0, const std::string &file_content = "")

Initialize the DP.

Parameters

model – [in] The name of the frozen model file.
gpu_rank – [in] The GPU rank. Default is 0.
file_content – [in] The content of the model file. If it is not empty, DP will read from the string instead of the file.

void print_summary(const std::string &pre) const

Print the DP summary to the screen.

Parameters: pre – [in] The prefix to each line.

void compute(ENERGYTYPE &ener, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const std::vector<VALUETYPE> &fparam = std::vector<VALUETYPE>(), const std::vector<VALUETYPE> &aparam = std::vector<VALUETYPE>())

Evaluate the energy, force and virial by using this DP.

Parameters

ener – [out] The system energy.
force – [out] The force on each atom.
virial – [out] The virial.
coord – [in] The coordinates of atoms. The array should be of size nframes x natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size nframes x 9.
fparam – [in] The frame parameter. The array can be of size : nframes x dim_fparam. dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam – [in] The atomic parameter The array can be of size : nframes x natoms x dim_aparam. natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. dim_aparam. Then all frames and atoms are provided with the same aparam.

void compute(ENERGYTYPE &ener, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const int nghost, const InputNlist &inlist, const int &ago, const std::vector<VALUETYPE> &fparam = std::vector<VALUETYPE>(), const std::vector<VALUETYPE> &aparam = std::vector<VALUETYPE>())

Evaluate the energy, force and virial by using this DP.

Parameters

ener – [out] The system energy.
force – [out] The force on each atom.
virial – [out] The virial.
coord – [in] The coordinates of atoms. The array should be of size nframes x natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size nframes x 9.
nghost – [in] The number of ghost atoms.
inlist – [in] The input neighbour list.
ago – [in] Update the internal neighbour list if ago is 0.
fparam – [in] The frame parameter. The array can be of size : nframes x dim_fparam. dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam – [in] The atomic parameter The array can be of size : nframes x natoms x dim_aparam. natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. dim_aparam. Then all frames and atoms are provided with the same aparam.

void compute(ENERGYTYPE &ener, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, std::vector<VALUETYPE> &atom_energy, std::vector<VALUETYPE> &atom_virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const std::vector<VALUETYPE> &fparam = std::vector<VALUETYPE>(), const std::vector<VALUETYPE> &aparam = std::vector<VALUETYPE>())

Evaluate the energy, force, virial, atomic energy, and atomic virial by using this DP.

Parameters

ener – [out] The system energy.
force – [out] The force on each atom.
virial – [out] The virial.
atom_energy – [out] The atomic energy.
atom_virial – [out] The atomic virial.
coord – [in] The coordinates of atoms. The array should be of size nframes x natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size nframes x 9.
fparam – [in] The frame parameter. The array can be of size : nframes x dim_fparam. dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam – [in] The atomic parameter The array can be of size : nframes x natoms x dim_aparam. natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. dim_aparam. Then all frames and atoms are provided with the same aparam.

void compute(ENERGYTYPE &ener, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, std::vector<VALUETYPE> &atom_energy, std::vector<VALUETYPE> &atom_virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const int nghost, const InputNlist &lmp_list, const int &ago, const std::vector<VALUETYPE> &fparam = std::vector<VALUETYPE>(), const std::vector<VALUETYPE> &aparam = std::vector<VALUETYPE>())

Evaluate the energy, force, virial, atomic energy, and atomic virial by using this DP.

Parameters

ener – [out] The system energy.
force – [out] The force on each atom.
virial – [out] The virial.
atom_energy – [out] The atomic energy.
atom_virial – [out] The atomic virial.
coord – [in] The coordinates of atoms. The array should be of size nframes x natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size nframes x 9.
nghost – [in] The number of ghost atoms.
lmp_list – [in] The input neighbour list.
ago – [in] Update the internal neighbour list if ago is 0.
fparam – [in] The frame parameter. The array can be of size : nframes x dim_fparam. dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam – [in] The atomic parameter The array can be of size : nframes x natoms x dim_aparam. natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. dim_aparam. Then all frames and atoms are provided with the same aparam.

inline VALUETYPE cutoff() const

Get the cutoff radius.

Returns: The cutoff radius.

inline int numb_types() const

Get the number of types.

Returns: The number of types.

inline int dim_fparam() const

Get the dimension of the frame parameter.

Returns: The dimension of the frame parameter.

inline int dim_aparam() const

Get the dimension of the atomic parameter.

Returns: The dimension of the atomic parameter.

void get_type_map(std::string &type_map)

Get the type map (element name of the atom types) of this model.

Parameters: type_map – [out] The type map of this model.

Class DeepPotModelDevi

Defined in File DeepPot.h

Class Documentation

class DeepPotModelDevi

Public Functions

DeepPotModelDevi(): DP model deviation constructor without initialization.

~DeepPotModelDevi()

DeepPotModelDevi(const std::vector<std::string> &models, const int &gpu_rank = 0, const std::vector<std::string> &file_contents = std::vector<std::string>())

DP model deviation constructor with initialization.

Parameters

models – [in] The names of the frozen model files.
gpu_rank – [in] The GPU rank. Default is 0.
file_contents – [in] The contents of the model files. If it is not empty, DP will read from the strings instead of the files.

void init(const std::vector<std::string> &models, const int &gpu_rank = 0, const std::vector<std::string> &file_contents = std::vector<std::string>())

Initialize the DP model deviation contrcutor.

Parameters

models – [in] The names of the frozen model files.
gpu_rank – [in] The GPU rank. Default is 0.
file_contents – [in] The contents of the model files. If it is not empty, DP will read from the strings instead of the files.

void compute(std::vector<ENERGYTYPE> &all_ener, std::vector<std::vector<VALUETYPE>> &all_force, std::vector<std::vector<VALUETYPE>> &all_virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const int nghost, const InputNlist &lmp_list, const int &ago, const std::vector<VALUETYPE> &fparam = std::vector<VALUETYPE>(), const std::vector<VALUETYPE> &aparam = std::vector<VALUETYPE>())

Evaluate the energy, force and virial by using these DP models.

Parameters

all_ener – [out] The system energies of all models.
all_force – [out] The forces on each atom of all models.
all_virial – [out] The virials of all models.
coord – [in] The coordinates of atoms. The array should be of size nframes x natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size nframes x 9.
nghost – [in] The number of ghost atoms.
lmp_list – [in] The input neighbour list.
ago – [in] Update the internal neighbour list if ago is 0.
fparam – [in] The frame parameter. The array can be of size : nframes x dim_fparam. dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam – [in] The atomic parameter The array can be of size : nframes x natoms x dim_aparam. natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. dim_aparam. Then all frames and atoms are provided with the same aparam.

void compute(std::vector<ENERGYTYPE> &all_ener, std::vector<std::vector<VALUETYPE>> &all_force, std::vector<std::vector<VALUETYPE>> &all_virial, std::vector<std::vector<VALUETYPE>> &all_atom_energy, std::vector<std::vector<VALUETYPE>> &all_atom_virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const int nghost, const InputNlist &lmp_list, const int &ago, const std::vector<VALUETYPE> &fparam = std::vector<VALUETYPE>(), const std::vector<VALUETYPE> &aparam = std::vector<VALUETYPE>())

Evaluate the energy, force, virial, atomic energy, and atomic virial by using these DP models.

Parameters

all_ener – [out] The system energies of all models.
all_force – [out] The forces on each atom of all models.
all_virial – [out] The virials of all models.
all_atom_energy – [out] The atomic energies of all models.
all_atom_virial – [out] The atomic virials of all models.
coord – [in] The coordinates of atoms. The array should be of size nframes x natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size nframes x 9.
nghost – [in] The number of ghost atoms.
lmp_list – [in] The input neighbour list.
ago – [in] Update the internal neighbour list if ago is 0.
fparam – [in] The frame parameter. The array can be of size : nframes x dim_fparam. dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam – [in] The atomic parameter The array can be of size : nframes x natoms x dim_aparam. natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. dim_aparam. Then all frames and atoms are provided with the same aparam.

inline VALUETYPE cutoff() const

Get the cutoff radius.

Returns: The cutoff radius.

inline int numb_types() const

Get the number of types.

Returns: The number of types.

inline int dim_fparam() const

Get the dimension of the frame parameter.

Returns: The dimension of the frame parameter.

inline int dim_aparam() const

Get the dimension of the atomic parameter.

Returns: The dimension of the atomic parameter.

void compute_avg(ENERGYTYPE &dener, const std::vector<ENERGYTYPE> &all_energy)

Compute the average energy.

Parameters

dener – [out] The average energy.
all_energy – [in] The energies of all models.

void compute_avg(VALUETYPE &dener, const std::vector<VALUETYPE> &all_energy)

Compute the average energy.

Parameters

dener – [out] The average energy.
all_energy – [in] The energies of all models.

void compute_avg(std::vector<VALUETYPE> &avg, const std::vector<std::vector<VALUETYPE>> &xx)

Compute the average of vectors.

Parameters

avg – [out] The average of vectors.
xx – [in] The vectors of all models.

void compute_std(std::vector<VALUETYPE> &std, const std::vector<VALUETYPE> &avg, const std::vector<std::vector<VALUETYPE>> &xx, const int &stride)

Compute the standard deviation of vectors.

Parameters

std – [out] The standard deviation of vectors.
avg – [in] The average of vectors.
xx – [in] The vectors of all models.
stride – [in] The stride to compute the deviation.

void compute_relative_std(std::vector<VALUETYPE> &std, const std::vector<VALUETYPE> &avg, const VALUETYPE eps, const int &stride)

Compute the relative standard deviation of vectors.

Parameters

std – [out] The standard deviation of vectors.
avg – [in] The average of vectors.
eps – [in] The level parameter for computing the deviation.
stride – [in] The stride to compute the deviation.

void compute_std_e(std::vector<VALUETYPE> &std, const std::vector<VALUETYPE> &avg, const std::vector<std::vector<VALUETYPE>> &xx)

Compute the standard deviation of atomic energies.

Parameters

std – [out] The standard deviation of atomic energies.
avg – [in] The average of atomic energies.
xx – [in] The vectors of all atomic energies.

void compute_std_f(std::vector<VALUETYPE> &std, const std::vector<VALUETYPE> &avg, const std::vector<std::vector<VALUETYPE>> &xx)

Compute the standard deviation of forces.

Parameters

std – [out] The standard deviation of forces.
avg – [in] The average of forces.
xx – [in] The vectors of all forces.

void compute_relative_std_f(std::vector<VALUETYPE> &std, const std::vector<VALUETYPE> &avg, const VALUETYPE eps)

Compute the relative standard deviation of forces.

Parameters

std – [out] The relative standard deviation of forces.
avg – [in] The relative average of forces.
eps – [in] The level parameter for computing the deviation.

Class DeepTensor

Defined in File DeepTensor.h

Class Documentation

class DeepTensor

Deep Tensor.

Public Functions

DeepTensor(): Deep Tensor constructor without initialization.

~DeepTensor()

DeepTensor(const std::string &model, const int &gpu_rank = 0, const std::string &name_scope = "")

Deep Tensor constructor with initialization..

Parameters

model – [in] The name of the frozen model file.
gpu_rank – [in] The GPU rank. Default is 0.
name_scope – [in] Name scopes of operations.

void init(const std::string &model, const int &gpu_rank = 0, const std::string &name_scope = "")

Initialize the Deep Tensor.

Parameters

model – [in] The name of the frozen model file.
gpu_rank – [in] The GPU rank. Default is 0.
name_scope – [in] Name scopes of operations.

void print_summary(const std::string &pre) const

Print the DP summary to the screen.

Parameters: pre – [in] The prefix to each line.

void compute(std::vector<VALUETYPE> &value, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box)

Evaluate the value by using this model.

Parameters

value – [out] The value to evalute, usually would be the atomic tensor.
coord – [in] The coordinates of atoms. The array should be of size natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size 9.

void compute(std::vector<VALUETYPE> &value, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const int nghost, const InputNlist &inlist)

Evaluate the value by using this model.

Parameters

value – [out] The value to evalute, usually would be the atomic tensor.
coord – [in] The coordinates of atoms. The array should be of size natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size 9.
nghost – [in] The number of ghost atoms.
inlist – [in] The input neighbour list.

void compute(std::vector<VALUETYPE> &global_tensor, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box)

Evaluate the global tensor and component-wise force and virial.

Parameters

global_tensor – [out] The global tensor to evalute.
force – [out] The component-wise force of the global tensor, size odim x natoms x 3.
virial – [out] The component-wise virial of the global tensor, size odim x 9.
coord – [in] The coordinates of atoms. The array should be of size natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size 9.

void compute(std::vector<VALUETYPE> &global_tensor, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const int nghost, const InputNlist &inlist)

Evaluate the global tensor and component-wise force and virial.

Parameters

global_tensor – [out] The global tensor to evalute.
force – [out] The component-wise force of the global tensor, size odim x natoms x 3.
virial – [out] The component-wise virial of the global tensor, size odim x 9.
coord – [in] The coordinates of atoms. The array should be of size natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size 9.
nghost – [in] The number of ghost atoms.
inlist – [in] The input neighbour list.

void compute(std::vector<VALUETYPE> &global_tensor, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, std::vector<VALUETYPE> &atom_tensor, std::vector<VALUETYPE> &atom_virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box)

Evaluate the global tensor and component-wise force and virial.

Parameters

global_tensor – [out] The global tensor to evalute.
force – [out] The component-wise force of the global tensor, size odim x natoms x 3.
virial – [out] The component-wise virial of the global tensor, size odim x 9.
atom_tensor – [out] The atomic tensor value of the model, size natoms x odim.
atom_virial – [out] The component-wise atomic virial of the global tensor, size odim x natoms x 9.
coord – [in] The coordinates of atoms. The array should be of size natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size 9.

void compute(std::vector<VALUETYPE> &global_tensor, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, std::vector<VALUETYPE> &atom_tensor, std::vector<VALUETYPE> &atom_virial, const std::vector<VALUETYPE> &coord, const std::vector<int> &atype, const std::vector<VALUETYPE> &box, const int nghost, const InputNlist &inlist)

Evaluate the global tensor and component-wise force and virial.

Parameters

global_tensor – [out] The global tensor to evalute.
force – [out] The component-wise force of the global tensor, size odim x natoms x 3.
virial – [out] The component-wise virial of the global tensor, size odim x 9.
atom_tensor – [out] The atomic tensor value of the model, size natoms x odim.
atom_virial – [out] The component-wise atomic virial of the global tensor, size odim x natoms x 9.
coord – [in] The coordinates of atoms. The array should be of size natoms x 3.
atype – [in] The atom types. The list should contain natoms ints.
box – [in] The cell of the region. The array should be of size 9.
nghost – [in] The number of ghost atoms.
inlist – [in] The input neighbour list.

inline VALUETYPE cutoff() const

Get the cutoff radius.

Returns: The cutoff radius.

inline int numb_types() const

Get the number of types.

Returns: The number of types.

inline int output_dim() const

Get the output dimension.

Returns: The output dimension.

inline const std::vector<int> &sel_types() const

Class DipoleChargeModifier

Defined in File DataModifier.h

Class Documentation

class DipoleChargeModifier

Public Functions

DipoleChargeModifier()

DipoleChargeModifier(const std::string &model, const int &gpu_rank = 0, const std::string &name_scope = "")

~DipoleChargeModifier()

void init(const std::string &model, const int &gpu_rank = 0, const std::string &name_scope = "")

void print_summary(const std::string &pre) const

void compute(std::vector<VALUETYPE> &dfcorr_, std::vector<VALUETYPE> &dvcorr_, const std::vector<VALUETYPE> &dcoord_, const std::vector<int> &datype_, const std::vector<VALUETYPE> &dbox, const std::vector<std::pair<int, int>> &pairs, const std::vector<VALUETYPE> &delef_, const int nghost, const InputNlist &lmp_list)

inline VALUETYPE cutoff() const

inline int numb_types() const

inline std::vector<int> sel_types() const

Functions

Function deepmd::check_status

Defined in File common.h

Function Documentation

void deepmd::check_status(const tensorflow::Status &status)

Check TensorFlow status. Exit if not OK.

Parameters: status – [in] TensorFlow status.

Function deepmd::convert_pbtxt_to_pb

Defined in File common.h

Function Documentation

void deepmd::convert_pbtxt_to_pb(std::string fn_pb_txt, std::string fn_pb)

Convert pbtxt to pb.

Parameters

fn_pb_txt – [in] Filename of the pb txt file.
fn_pb – [out] Filename of the pb file.

Function deepmd::get_env_nthreads

Defined in File common.h

Function Documentation

void deepmd::get_env_nthreads(int &num_intra_nthreads, int &num_inter_nthreads)

Get the number of threads from the environment variable.

A warning will be thrown if environmental variables are not set.

Parameters

num_intra_nthreads – [out] The number of intra threads. Read from TF_INTRA_OP_PARALLELISM_THREADS.
num_inter_nthreads – [out] The number of inter threads. Read from TF_INTER_OP_PARALLELISM_THREADS.

Function deepmd::load_op_library

Defined in File common.h

Function Documentation

void deepmd::load_op_library(): Dynamically load OP library. This should be called before loading graphs.

Function deepmd::model_compatable

Defined in File common.h

Function Documentation

bool deepmd::model_compatable(std::string &model_version)

Check if the model version is supported.

Parameters: model_version – [in] The model version.
Returns: Whether the model is supported (true or false).

Function deepmd::name_prefix

Defined in File common.h

Function Documentation

std::string deepmd::name_prefix(const std::string &name_scope)

Function deepmd::read_file_to_string

Defined in File common.h

Function Documentation

void deepmd::read_file_to_string(std::string model, std::string &file_content)

Read model file to a string.

Parameters

model – [in] Path to the model.
file_content – [out] Content of the model file.

Function deepmd::select_by_type

Defined in File common.h

Function Documentation

void deepmd::select_by_type(std::vector<int> &fwd_map, std::vector<int> &bkw_map, int &nghost_real, const std::vector<VALUETYPE> &dcoord_, const std::vector<int> &datype_, const int &nghost, const std::vector<int> &sel_type_)

Template Function deepmd::select_map(std::vector<VT>&, const std::vector<VT>&, const std::vector<int>&, const int&)

Defined in File common.h

Function Documentation

template<typename VT> void deepmd::select_map(std::vector<VT> &out, const std::vector<VT> &in, const std::vector<int> &fwd_map, const int &stride)

Template Function deepmd::select_map(typename std::vector<VT>::iterator, const typename std::vector<VT>::const_iterator, const std::vector<int>&, const int&)

Defined in File common.h

Function Documentation

template<typename VT> void deepmd::select_map(typename std::vector<VT>::iterator out, const typename std::vector<VT>::const_iterator in, const std::vector<int> &fwd_map, const int &stride)

Template Function deepmd::select_map_inv(std::vector<VT>&, const std::vector<VT>&, const std::vector<int>&, const int&)

Defined in File common.h

Function Documentation

template<typename VT> void deepmd::select_map_inv(std::vector<VT> &out, const std::vector<VT> &in, const std::vector<int> &fwd_map, const int &stride)

Template Function deepmd::select_map_inv(typename std::vector<VT>::iterator, const typename std::vector<VT>::const_iterator, const std::vector<int>&, const int&)

Defined in File common.h

Function Documentation

template<typename VT> void deepmd::select_map_inv(typename std::vector<VT>::iterator out, const typename std::vector<VT>::const_iterator in, const std::vector<int> &fwd_map, const int &stride)

Function deepmd::select_real_atoms

Defined in File common.h

Function Documentation

void deepmd::select_real_atoms(std::vector<int> &fwd_map, std::vector<int> &bkw_map, int &nghost_real, const std::vector<VALUETYPE> &dcoord_, const std::vector<int> &datype_, const int &nghost, const int &ntypes)

Template Function deepmd::session_get_scalar

Defined in File common.h

Function Documentation

template<typename VT> VT deepmd::session_get_scalar(tensorflow::Session *session, const std::string name, const std::string scope = "")

Template Function deepmd::session_get_vector

Defined in File common.h

Function Documentation

template<typename VT> void deepmd::session_get_vector(std::vector<VT> &o_vec, tensorflow::Session *session, const std::string name_, const std::string scope = "")

Function deepmd::session_input_tensors(std::vector<std::pair<std::string, tensorflow::Tensor>>&, const std::vector<VALUETYPE>&, const int&, const std::vector<int>&, const std::vector<VALUETYPE>&, const VALUETYPE&, const std::vector<VALUETYPE>&, const std::vector<VALUETYPE>&, const deepmd::AtomMap<VALUETYPE>&, const std::string)

Defined in File common.h

Function Documentation

int deepmd::session_input_tensors(std::vector<std::pair<std::string, tensorflow::Tensor>> &input_tensors, const std::vector<VALUETYPE> &dcoord_, const int &ntypes, const std::vector<int> &datype_, const std::vector<VALUETYPE> &dbox, const VALUETYPE &cell_size, const std::vector<VALUETYPE> &fparam_, const std::vector<VALUETYPE> &aparam_, const deepmd::AtomMap<VALUETYPE> &atommap, const std::string scope = "")

Function deepmd::session_input_tensors(std::vector<std::pair<std::string, tensorflow::Tensor>>&, const std::vector<VALUETYPE>&, const int&, const std::vector<int>&, const std::vector<VALUETYPE>&, InputNlist&, const std::vector<VALUETYPE>&, const std::vector<VALUETYPE>&, const deepmd::AtomMap<VALUETYPE>&, const int, const int, const std::string)

Defined in File common.h

Function Documentation

int deepmd::session_input_tensors(std::vector<std::pair<std::string, tensorflow::Tensor>> &input_tensors, const std::vector<VALUETYPE> &dcoord_, const int &ntypes, const std::vector<int> &datype_, const std::vector<VALUETYPE> &dbox, InputNlist &dlist, const std::vector<VALUETYPE> &fparam_, const std::vector<VALUETYPE> &aparam_, const deepmd::AtomMap<VALUETYPE> &atommap, const int nghost, const int ago, const std::string scope = "")

Typedefs

Typedef deepmd::ENERGYTYPE

Defined in File common.h

Typedef Documentation

typedef double deepmd::ENERGYTYPE

Typedef deepmd::STRINGTYPE

Defined in File tf_private.h

Typedef Documentation

typedef std::string deepmd::STRINGTYPE

Typedef deepmd::VALUETYPE

Defined in File common.h

Typedef Documentation

typedef float deepmd::VALUETYPE

Core API

Class Hierarchy

Full API

Namespaces

Namespace deepmd

Contents

Classes
Functions
Variables

Classes

Functions

Variables

Variable deepmd::ElectrostaticConvertion

Namespace std

Classes and Structs

Struct deepmd_exception

Defined in File errors.h

Inheritance Relationships

Base Type

public std::runtime_error

Derived Type

public deepmd::deepmd_exception_oom (Struct deepmd_exception_oom)

Struct Documentation

struct deepmd_exception : public std::runtime_error

General DeePMD-kit exception. Throw if anything doesn’t work.

Subclassed by deepmd::deepmd_exception_oom

Public Functions

inline deepmd_exception()

inline deepmd_exception(const std::string &msg)

Struct deepmd_exception_oom

Defined in File errors.h

Inheritance Relationships

Base Type

public deepmd::deepmd_exception (Struct deepmd_exception)

Struct Documentation

struct deepmd_exception_oom : public deepmd::deepmd_exception 

Public Functions

inline deepmd_exception_oom()

inline deepmd_exception_oom(const std::string &msg)

Template Struct EwaldParameters

Defined in File ewald.h

Struct Documentation

template<typename VALUETYPE> struct EwaldParameters

Public Members

VALUETYPE rcut = 6.0

VALUETYPE beta = 2

VALUETYPE spacing = 4

Struct InputNlist

Defined in File neighbor_list.h

Struct Documentation

struct InputNlist

Construct InputNlist with the input LAMMPS nbor list info.

Public Functions

inline InputNlist()

inline InputNlist(int inum_, int *ilist_, int *numneigh_, int **firstneigh_)

inline ~InputNlist()

Public Members

int inum: Number of core region atoms.

int *ilist: Array stores the core region atom’s index.

int *numneigh: Array stores the core region atom’s neighbor atom number.

int **firstneigh: Array stores the core region atom’s neighbor index.

Template Struct Region

Defined in File region.h

Struct Documentation

template<typename FPTYPE> struct Region

Public Functions

Region()

~Region()

Public Members

FPTYPE *boxt

FPTYPE *rec_boxt

Template Struct DescrptSeRGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct DescrptSeRGPUExecuteFunctor

Public Functions

void operator()(const FPTYPE *coord, const int *type, const int *ilist, const int *jrange, const int *jlist, int *array_int, unsigned long long *array_longlong, const FPTYPE *avg, const FPTYPE *std, FPTYPE *descript, FPTYPE *descript_deriv, FPTYPE *rij, int *nlist, const int nloc, const int nall, const int nnei, const int ndescrpt, const float rcut_r, const float rcut_r_smth, const std::vector<int> sec_a, const bool fill_nei_a, const int MAGIC_NUMBER)

Template Struct GeluGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct GeluGPUExecuteFunctor

Public Functions

void operator()(const FPTYPE *in, FPTYPE *out, const int size)

Template Struct GeluGradGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct GeluGradGPUExecuteFunctor

Public Functions

void operator()(const FPTYPE *dy, const FPTYPE *in, FPTYPE *out, const int size)

Template Struct GeluGradGradGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct GeluGradGradGPUExecuteFunctor

Public Functions

void operator()(const FPTYPE *dy, const FPTYPE *dy_, const FPTYPE *in, FPTYPE *out, const int size)

Template Struct ProdForceSeAGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct ProdForceSeAGPUExecuteFunctor

Public Functions

void operator()(FPTYPE *force, const FPTYPE *net_derive, const FPTYPE *in_deriv, const int *nlist, const int nloc, const int nall, const int nnei, const int ndescrpt, const int n_a_sel, const int n_a_shift)

Template Struct ProdForceSeRGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct ProdForceSeRGPUExecuteFunctor

Public Functions

void operator()(FPTYPE *force, const FPTYPE *net_derive, const FPTYPE *in_deriv, const int *nlist, const int nloc, const int nall, const int nnei, const int ndescrpt)

Template Struct ProdVirialSeAGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct ProdVirialSeAGPUExecuteFunctor

Public Functions

void operator()(FPTYPE *virial, FPTYPE *atom_virial, const FPTYPE *net_deriv, const FPTYPE *in_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nall, const int nnei, const int ndescrpt, const int n_a_sel, const int n_a_shift)

Template Struct ProdVirialSeRGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct ProdVirialSeRGPUExecuteFunctor

Public Functions

void operator()(FPTYPE *virial, FPTYPE *atom_virial, const FPTYPE *net_deriv, const FPTYPE *in_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nall, const int nnei, const int ndescrpt)

Template Struct TabulateCheckerGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct TabulateCheckerGPUExecuteFunctor

Public Functions

void operator()(const FPTYPE *table_info, const FPTYPE *in, int *out, const int nloc, const int nnei)

Template Struct TabulateFusionGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct TabulateFusionGPUExecuteFunctor

Public Functions

void operator()(const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *in, const FPTYPE *ff, const int nloc, const int nnei, const int last_layer_size, FPTYPE *out)

Template Struct TabulateFusionGradGPUExecuteFunctor

Defined in File DeviceFunctor.h

Struct Documentation

template<typename FPTYPE> struct TabulateFusionGradGPUExecuteFunctor

Public Functions

void operator()(const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *in, const FPTYPE *ff, const FPTYPE *dy, const int nloc, const int nnei, const int last_layer_size, FPTYPE *dy_dx, FPTYPE *dy_df)

Template Class SimulationRegion

Defined in File SimulationRegion.h

Class Documentation

template<typename VALUETYPE> class SimulationRegion

Public Functions

inline void reinitBox(const double *boxv)

inline void affineTransform(const double *affine_map)

inline void reinitOrigin(const double *orig)

inline void reinitOrigin(const std::vector<double> &orig)

void backup()

void recover()

SimulationRegion()

~SimulationRegion()

inline double *getBoxTensor()

inline const double *getBoxTensor() const

inline double *getRecBoxTensor()

inline const double *getRecBoxTensor() const

inline double *getBoxOrigin()

inline const double *getBoxOrigin() const

inline double getVolume() const

inline void toFaceDistance(double *dd) const

inline void phys2Inter(double *i_v, const VALUETYPE *p_v) const

inline void inter2Phys(VALUETYPE *p_v, const double *i_v) const

inline bool isPeriodic(const int dim) const

inline double *getShiftVec(const int index = 0)

inline const double *getShiftVec(const int index = 0) const

inline int getShiftIndex(const int *idx) const

inline int getNullShiftIndex() const

inline void shiftCoord(const int *idx, VALUETYPE &x, VALUETYPE &y, VALUETYPE &z) const

inline void diffNearestNeighbor(const VALUETYPE *r0, const VALUETYPE *r1, VALUETYPE *phys) const

inline virtual void diffNearestNeighbor(const VALUETYPE x0, const VALUETYPE y0, const VALUETYPE z0, const VALUETYPE x1, const VALUETYPE y1, const VALUETYPE z1, VALUETYPE &dx, VALUETYPE &dy, VALUETYPE &dz) const

inline virtual void diffNearestNeighbor(const VALUETYPE x0, const VALUETYPE y0, const VALUETYPE z0, const VALUETYPE x1, const VALUETYPE y1, const VALUETYPE z1, VALUETYPE &dx, VALUETYPE &dy, VALUETYPE &dz, int &shift_x, int &shift_y, int &shift_z) const

inline virtual void diffNearestNeighbor(const VALUETYPE x0, const VALUETYPE y0, const VALUETYPE z0, const VALUETYPE x1, const VALUETYPE y1, const VALUETYPE z1, VALUETYPE &dx, VALUETYPE &dy, VALUETYPE &dz, VALUETYPE &shift_x, VALUETYPE &shift_y, VALUETYPE &shift_z) const

Public Static Functions

static inline int compactIndex(const int *idx)

static inline int getNumbShiftVec()

static inline int getShiftVecTotalSize()

Protected Functions

void computeShiftVec()

inline double *getInterShiftVec(const int index = 0)

inline const double *getInterShiftVec(const int index = 0) const

Protected Attributes

double shift_vec[shift_vec_size]

double inter_shift_vec[shift_vec_size]

Protected Static Functions

static inline int index3to1(const int tx, const int ty, const int tz)

Protected Static Attributes

static const int SPACENDIM = 3

static const int DBOX_XX = 1

static const int DBOX_YY = 1

static const int DBOX_ZZ = 1

static const int NBOX_XX = DBOX_XX * 2 + 1

static const int NBOX_YY = DBOX_YY * 2 + 1

static const int NBOX_ZZ = DBOX_ZZ * 2 + 1

static const int shift_info_size = NBOX_XX * NBOX_YY * NBOX_ZZ 

static const int shift_vec_size = SPACENDIM * shift_info_size 

Functions

Function build_nlist(std::vector<std::vector<int>>&, std::vector<std::vector<int>>&, const std::vector<double>&, const int&, const double&, const double&, const std::vector<int>&, const std::vector<int>&, const std::vector<int>&, const std::vector<int>&, const SimulationRegion<double>&, const std::vector<int>&)

Defined in File neighbor_list.h

Function Documentation

void build_nlist(std::vector<std::vector<int>> &nlist0, std::vector<std::vector<int>> &nlist1, const std::vector<double> &coord, const int &nloc, const double &rc0, const double &rc1, const std::vector<int> &nat_stt_, const std::vector<int> &nat_end_, const std::vector<int> &ext_stt_, const std::vector<int> &ext_end_, const SimulationRegion<double> &region, const std::vector<int> &global_grid)

Function build_nlist(std::vector<std::vector<int>>&, std::vector<std::vector<int>>&, const std::vector<double>&, const double&, const double&, const std::vector<int>&, const SimulationRegion<double>&)

Defined in File neighbor_list.h

Function Documentation

void build_nlist(std::vector<std::vector<int>> &nlist0, std::vector<std::vector<int>> &nlist1, const std::vector<double> &coord, const double &rc0, const double &rc1, const std::vector<int> &grid, const SimulationRegion<double> &region)

Function build_nlist(std::vector<std::vector<int>>&, std::vector<std::vector<int>>&, const std::vector<double>&, const std::vector<int>&, const std::vector<int>&, const double&, const double&, const std::vector<int>&, const SimulationRegion<double>&)

Defined in File neighbor_list.h

Function Documentation

void build_nlist(std::vector<std::vector<int>> &nlist0, std::vector<std::vector<int>> &nlist1, const std::vector<double> &coord, const std::vector<int> &sel0, const std::vector<int> &sel1, const double &rc0, const double &rc1, const std::vector<int> &grid, const SimulationRegion<double> &region)

Function build_nlist(std::vector<std::vector<int>>&, std::vector<std::vector<int>>&, const std::vector<double>&, const double&, const double&, const SimulationRegion<double> *)

Defined in File neighbor_list.h

Function Documentation

void build_nlist(std::vector<std::vector<int>> &nlist0, std::vector<std::vector<int>> &nlist1, const std::vector<double> &coord, const double &rc0_, const double &rc1_, const SimulationRegion<double> *region = NULL)

Function compute_descriptor(std::vector<double>&, std::vector<double>&, std::vector<double>&, const std::vector<double>&, const int&, const std::vector<int>&, const SimulationRegion<double>&, const bool&, const int&, const std::vector<int>&, const std::vector<int>&, const std::vector<int>&, const std::vector<int>&, const int, const int, const int, const int)

Defined in File ComputeDescriptor.h

Function Documentation

inline void compute_descriptor(std::vector<double> &descrpt_a, std::vector<double> &descrpt_r, std::vector<double> &rot_mat, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const int &i_idx, const std::vector<int> &fmt_nlist_a, const std::vector<int> &fmt_nlist_r, const std::vector<int> &sec_a, const std::vector<int> &sec_r, const int axis0_type, const int axis0_idx, const int axis1_type, const int axis1_idx)

Function compute_descriptor(std::vector<double>&, std::vector<double>&, std::vector<double>&, std::vector<double>&, std::vector<double>&, std::vector<double>&, std::vector<double>&, const std::vector<double>&, const int&, const std::vector<int>&, const SimulationRegion<double>&, const bool&, const int&, const std::vector<int>&, const std::vector<int>&, const std::vector<int>&, const std::vector<int>&, const int, const int, const int, const int)

Defined in File ComputeDescriptor.h

Function Documentation

inline void compute_descriptor(std::vector<double> &descrpt_a, std::vector<double> &descrpt_a_deriv, std::vector<double> &descrpt_r, std::vector<double> &descrpt_r_deriv, std::vector<double> &rij_a, std::vector<double> &rij_r, std::vector<double> &rot_mat, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const int &i_idx, const std::vector<int> &fmt_nlist_a, const std::vector<int> &fmt_nlist_r, const std::vector<int> &sec_a, const std::vector<int> &sec_r, const int axis0_type, const int axis0_idx, const int axis1_type, const int axis1_idx)

Function compute_descriptor_se_a_ef_para

Defined in File ComputeDescriptor.h

Function Documentation

inline void compute_descriptor_se_a_ef_para(std::vector<double> &descrpt_a, std::vector<double> &descrpt_a_deriv, std::vector<double> &rij_a, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const std::vector<double> &efield, const int &i_idx, const std::vector<int> &fmt_nlist_a, const std::vector<int> &sec_a, const double &rmin, const double &rmax)

Function compute_descriptor_se_a_ef_vert

Defined in File ComputeDescriptor.h

Function Documentation

inline void compute_descriptor_se_a_ef_vert(std::vector<double> &descrpt_a, std::vector<double> &descrpt_a_deriv, std::vector<double> &rij_a, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const std::vector<double> &efield, const int &i_idx, const std::vector<int> &fmt_nlist_a, const std::vector<int> &sec_a, const double &rmin, const double &rmax)

Function compute_descriptor_se_a_extf

Defined in File ComputeDescriptor.h

Function Documentation

inline void compute_descriptor_se_a_extf(std::vector<double> &descrpt_a, std::vector<double> &descrpt_a_deriv, std::vector<double> &rij_a, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const std::vector<double> &efield, const int &i_idx, const std::vector<int> &fmt_nlist_a, const std::vector<int> &sec_a, const double &rmin, const double &rmax)

Function compute_dRdT

Defined in File ComputeDescriptor.h

Function Documentation

Warning

doxygenfunction: Unable to resolve function “compute_dRdT” with arguments (double (*), const double*, const double*, const double*) in doxygen xml output for project “core” from directory: _build/core/xml/. Potential matches:

- void compute_dRdT(double (*dRdT)[9], const double *r1, const double *r2, const double *rot)

Function compute_dRdT_1

Defined in File ComputeDescriptor.h

Function Documentation

Warning

doxygenfunction: Unable to resolve function “compute_dRdT_1” with arguments (double (*), const double*, const double*, const double*) in doxygen xml output for project “core” from directory: _build/core/xml/. Potential matches:

- void compute_dRdT_1(double (*dRdT)[9], const double *r1, const double *r2, const double *rot)

Function compute_dRdT_2

Defined in File ComputeDescriptor.h

Function Documentation

Warning

doxygenfunction: Unable to resolve function “compute_dRdT_2” with arguments (double (*), const double*, const double*, const double*) in doxygen xml output for project “core” from directory: _build/core/xml/. Potential matches:

- void compute_dRdT_2(double (*dRdT)[9], const double *r1, const double *r2, const double *rot)

Function copy_coord

Defined in File neighbor_list.h

Function Documentation

void copy_coord(std::vector<double> &out_c, std::vector<int> &out_t, std::vector<int> &mapping, std::vector<int> &ncell, std::vector<int> &ngcell, const std::vector<double> &in_c, const std::vector<int> &in_t, const double &rc, const SimulationRegion<double> &region)

Template Function deepmd::build_nlist_cpu

Defined in File neighbor_list.h

Function Documentation

template<typename FPTYPE> int deepmd::build_nlist_cpu(InputNlist &nlist, int *max_list_size, const FPTYPE *c_cpy, const int &nloc, const int &nall, const int &mem_size, const float &rcut)

Template Function deepmd::build_nlist_gpu

Defined in File neighbor_list.h

Function Documentation

template<typename FPTYPE> int deepmd::build_nlist_gpu(InputNlist &nlist, int *max_list_size, int *nlist_data, const FPTYPE *c_cpy, const int &nloc, const int &nall, const int &mem_size, const float &rcut)

Template Function deepmd::compute_cell_info

Defined in File coord.h

Function Documentation

template<typename FPTYPE> void deepmd::compute_cell_info(int *cell_info, const float &rcut, const deepmd::Region<FPTYPE> &region)

Function deepmd::convert_nlist

Defined in File neighbor_list.h

Function Documentation

void deepmd::convert_nlist(InputNlist &to_nlist, std::vector<std::vector<int>> &from_nlist)

Construct the InputNlist with a two-dimensional vector.

Parameters

to_nlist – InputNlist struct which stores the neighbor information of the core region atoms.
from_nlist – Vector which stores the neighbor information of the core region atoms.

Function deepmd::convert_nlist_gpu_device

Defined in File neighbor_list.h

Function Documentation

void deepmd::convert_nlist_gpu_device(InputNlist &gpu_nlist, InputNlist &cpu_nlist, int *&gpu_memory, const int &max_nbor_size)

Convert the a host memory InputNlist to a device memory InputNlist.

Parameters

cpu_nlist – Host memory InputNlist struct which stores the neighbor information of the core region atoms
gpu_nlist – Device memory InputNlist struct which stores the neighbor information of the core region atoms
gpu_memory – Device array which stores the elements of gpu_nlist
max_nbor_size –

Template Function deepmd::convert_to_inter_cpu

Defined in File region.h

Function Documentation

template<typename FPTYPE> void deepmd::convert_to_inter_cpu(FPTYPE *ri, const Region<FPTYPE> &region, const FPTYPE *rp)

Template Function deepmd::convert_to_inter_gpu

Defined in File region.h

Function Documentation

template<typename FPTYPE> void deepmd::convert_to_inter_gpu(FPTYPE *ri, const Region<FPTYPE> &region, const FPTYPE *rp)

Template Function deepmd::convert_to_phys_cpu

Defined in File region.h

Function Documentation

template<typename FPTYPE> void deepmd::convert_to_phys_cpu(FPTYPE *rp, const Region<FPTYPE> &region, const FPTYPE *ri)

Template Function deepmd::convert_to_phys_gpu

Defined in File region.h

Function Documentation

template<typename FPTYPE> void deepmd::convert_to_phys_gpu(FPTYPE *rp, const Region<FPTYPE> &region, const FPTYPE *ri)

Template Function deepmd::copy_coord_cpu

Defined in File coord.h

Function Documentation

template<typename FPTYPE> int deepmd::copy_coord_cpu(FPTYPE *out_c, int *out_t, int *mapping, int *nall, const FPTYPE *in_c, const int *in_t, const int &nloc, const int &mem_nall, const float &rcut, const deepmd::Region<FPTYPE> &region)

Template Function deepmd::copy_coord_gpu

Defined in File coord.h

Function Documentation

template<typename FPTYPE> int deepmd::copy_coord_gpu(FPTYPE *out_c, int *out_t, int *mapping, int *nall, int *int_data, const FPTYPE *in_c, const int *in_t, const int &nloc, const int &mem_nall, const int &loc_cellnum, const int &total_cellnum, const int *cell_info, const deepmd::Region<FPTYPE> &region)

Function deepmd::cos_switch(const double&, const double&, const double&)

Defined in File switcher.h

Function Documentation

inline double deepmd::cos_switch(const double &xx, const double &rmin, const double &rmax)

Function deepmd::cos_switch(double&, double&, const double&, const double&, const double&)

Defined in File switcher.h

Function Documentation

inline void deepmd::cos_switch(double &vv, double &dd, const double &xx, const double &rmin, const double &rmax)

Template Function deepmd::cprod

Defined in File utilities.h

Function Documentation

template<typename TYPE> inline void deepmd::cprod(const TYPE *r0, const TYPE *r1, TYPE *r2)

Function deepmd::cum_sum

Defined in File utilities.h

Function Documentation

void deepmd::cum_sum(std::vector<int> &sec, const std::vector<int> &n_sel)

Template Function deepmd::delete_device_memory

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::delete_device_memory(FPTYPE *&device)

Template Function deepmd::dot1

Defined in File utilities.h

Function Documentation

template<typename TYPE> inline TYPE deepmd::dot1(const TYPE *r0, const TYPE *r1)

Template Function deepmd::dot2

Defined in File utilities.h

Function Documentation

template<typename TYPE> inline TYPE deepmd::dot2(const TYPE *r0, const TYPE *r1)

Template Function deepmd::dot3

Defined in File utilities.h

Function Documentation

template<typename TYPE> inline TYPE deepmd::dot3(const TYPE *r0, const TYPE *r1)

Template Function deepmd::dot4

Defined in File utilities.h

Function Documentation

template<typename TYPE> inline TYPE deepmd::dot4(const TYPE *r0, const TYPE *r1)

Template Function deepmd::dotmv3

Defined in File utilities.h

Function Documentation

template<typename TYPE> inline void deepmd::dotmv3(TYPE *vec_o, const TYPE *tensor, const TYPE *vec_i)

Function deepmd::DPGetDeviceCount

Defined in File gpu_cuda.h

Function Documentation

inline void deepmd::DPGetDeviceCount(int &gpu_num)

Function deepmd::DPSetDevice

Defined in File gpu_cuda.h

Function Documentation

inline cudaError_t deepmd::DPSetDevice(int rank)

Template Function deepmd::env_mat_a_cpu

Defined in File env_mat.h

Function Documentation

template<typename FPTYPE> void deepmd::env_mat_a_cpu(std::vector<FPTYPE> &descrpt_a, std::vector<FPTYPE> &descrpt_a_deriv, std::vector<FPTYPE> &rij_a, const std::vector<FPTYPE> &posi, const std::vector<int> &type, const int &i_idx, const std::vector<int> &fmt_nlist, const std::vector<int> &sec, const float &rmin, const float &rmax)

Template Function deepmd::env_mat_a_nvnmd_quantize_cpu

Defined in File env_mat_nvnmd.h

Function Documentation

Warning

doxygenfunction: Unable to resolve function “deepmd::env_mat_a_nvnmd_quantize_cpu” with arguments (std::vector<FPTYPE>&, std::vector<FPTYPE>&, std::vector<FPTYPE>&, const std::vector<FPTYPE>&, const std::vector<int>&, const int&, const std::vector<int>&, const std::vector<int>&, const float&, const float&, const FPTYPE) in doxygen xml output for project “core” from directory: _build/core/xml/. Potential matches:

- template<typename FPTYPE> void env_mat_a_nvnmd_quantize_cpu(std::vector<FPTYPE> &descrpt_a, std::vector<FPTYPE> &descrpt_a_deriv, std::vector<FPTYPE> &rij_a, const std::vector<FPTYPE> &posi, const std::vector<int> &type, const int &i_idx, const std::vector<int> &fmt_nlist, const std::vector<int> &sec, const float &rmin, const float &rmax, const FPTYPE precs[3])

Function deepmd::env_mat_nbor_update

Defined in File prod_env_mat.h

Function Documentation

void deepmd::env_mat_nbor_update(InputNlist &inlist, InputNlist &gpu_inlist, int &max_nbor_size, int *&nbor_list_dev, const int *mesh, const int size)

Template Function deepmd::env_mat_r_cpu

Defined in File env_mat.h

Function Documentation

template<typename FPTYPE> void deepmd::env_mat_r_cpu(std::vector<FPTYPE> &descrpt_a, std::vector<FPTYPE> &descrpt_a_deriv, std::vector<FPTYPE> &rij_a, const std::vector<FPTYPE> &posi, const std::vector<int> &type, const int &i_idx, const std::vector<int> &fmt_nlist_a, const std::vector<int> &sec_a, const float &rmin, const float &rmax)

Template Function deepmd::ewald_recp

Defined in File ewald.h

Function Documentation

template<typename VALUETYPE> void deepmd::ewald_recp(VALUETYPE &ener, std::vector<VALUETYPE> &force, std::vector<VALUETYPE> &virial, const std::vector<VALUETYPE> &coord, const std::vector<VALUETYPE> &charge, const deepmd::Region<VALUETYPE> &region, const EwaldParameters<VALUETYPE> &param)

Template Function deepmd::format_nbor_list_gpu_cuda

Defined in File fmt_nlist.h

Function Documentation

template<typename FPTYPE> void deepmd::format_nbor_list_gpu_cuda(int *nlist, const FPTYPE *coord, const int *type, const deepmd::InputNlist &gpu_inlist, int *array_int, uint_64 *array_longlong, const int max_nbor_size, const int nloc, const int nall, const float rcut, const std::vector<int> sec)

Template Function deepmd::format_nlist_cpu

Defined in File fmt_nlist.h

Function Documentation

template<typename FPTYPE> void deepmd::format_nlist_cpu(int *nlist, const InputNlist &in_nlist, const FPTYPE *coord, const int *type, const int nloc, const int nall, const float rcut, const std::vector<int> sec)

Function deepmd::free_nlist_gpu_device

Defined in File neighbor_list.h

Function Documentation

void deepmd::free_nlist_gpu_device(InputNlist &gpu_nlist)

Reclaim the allocated device memory of struct InputNlist.

Parameters: gpu_nlist – Device memory InputNlist struct which stores the neighbor information of the core region atoms

Template Function deepmd::gelu_cpu

Defined in File gelu.h

Function Documentation

template<typename FPTYPE> void deepmd::gelu_cpu(FPTYPE *out, const FPTYPE *xx, const int_64 size)

Template Function deepmd::gelu_gpu_cuda

Defined in File gelu.h

Function Documentation

template<typename FPTYPE> void deepmd::gelu_gpu_cuda(FPTYPE *out, const FPTYPE *xx, const int_64 size)

Template Function deepmd::gelu_grad_cpu

Defined in File gelu.h

Function Documentation

template<typename FPTYPE> void deepmd::gelu_grad_cpu(FPTYPE *out, const FPTYPE *xx, const FPTYPE *dy, const int_64 size)

Template Function deepmd::gelu_grad_gpu_cuda

Defined in File gelu.h

Function Documentation

template<typename FPTYPE> void deepmd::gelu_grad_gpu_cuda(FPTYPE *out, const FPTYPE *xx, const FPTYPE *dy, const int_64 size)

Template Function deepmd::gelu_grad_grad_cpu

Defined in File gelu.h

Function Documentation

template<typename FPTYPE> void deepmd::gelu_grad_grad_cpu(FPTYPE *out, const FPTYPE *xx, const FPTYPE *dy, const FPTYPE *dy_2, const int_64 size)

Template Function deepmd::gelu_grad_grad_gpu_cuda

Defined in File gelu.h

Function Documentation

template<typename FPTYPE> void deepmd::gelu_grad_grad_gpu_cuda(FPTYPE *out, const FPTYPE *xx, const FPTYPE *dy, const FPTYPE *dy_2, const int_64 size)

Template Function deepmd::init_region_cpu

Defined in File region.h

Function Documentation

template<typename FPTYPE> void deepmd::init_region_cpu(Region<FPTYPE> &region, const FPTYPE *boxt)

Template Function deepmd::invsqrt

Defined in File utilities.h

Function Documentation

template<typename TYPE> inline TYPE deepmd::invsqrt(const TYPE x)

Specialized Template Function deepmd::invsqrt< double >

Defined in File utilities.h

Function Documentation

template<> inline double deepmd::invsqrt<double>(const double x)

Specialized Template Function deepmd::invsqrt< float >

Defined in File utilities.h

Function Documentation

template<> inline float deepmd::invsqrt<float>(const float x)

Template Function deepmd::malloc_device_memory(FPTYPE *&, const std::vector<FPTYPE>&)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::malloc_device_memory(FPTYPE *&device, const std::vector<FPTYPE> &host)

Template Function deepmd::malloc_device_memory(FPTYPE *&, const int)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::malloc_device_memory(FPTYPE *&device, const int size)

Template Function deepmd::malloc_device_memory(FPTYPE *&, std::vector<FPTYPE>&)

Defined in File gpu_rocm.h

Function Documentation

template<typename FPTYPE> void deepmd::malloc_device_memory(FPTYPE *&device, std::vector<FPTYPE> &host)

Template Function deepmd::malloc_device_memory_sync(FPTYPE *&, const std::vector<FPTYPE>&)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::malloc_device_memory_sync(FPTYPE *&device, const std::vector<FPTYPE> &host)

Template Function deepmd::malloc_device_memory_sync(FPTYPE &, const FPTYPE , const int)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::malloc_device_memory_sync(FPTYPE *&device, const FPTYPE *host, const int size)

Template Function deepmd::malloc_device_memory_sync(FPTYPE *&, std::vector<FPTYPE>&)

Defined in File gpu_rocm.h

Function Documentation

template<typename FPTYPE> void deepmd::malloc_device_memory_sync(FPTYPE *&device, std::vector<FPTYPE> &host)

Template Function deepmd::map_aparam_cpu

Defined in File map_aparam.h

Function Documentation

template<typename FPTYPE> void deepmd::map_aparam_cpu(FPTYPE *output, const FPTYPE *aparam, const int *nlist, const int &nloc, const int &nnei, const int &numb_aparam)

Function deepmd::max_numneigh

Defined in File neighbor_list.h

Function Documentation

int deepmd::max_numneigh(const InputNlist &to_nlist)

Compute the max number of neighbors within the core region atoms.

Parameters: to_nlist – InputNlist struct which stores the neighbor information of the core region atoms.
Return values: max – number of neighbors
Returns: integer

Template Function deepmd::memcpy_device_to_host(const FPTYPE *, std::vector<FPTYPE>&)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::memcpy_device_to_host(const FPTYPE *device, std::vector<FPTYPE> &host)

Template Function deepmd::memcpy_device_to_host(const FPTYPE , FPTYPE , const int)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::memcpy_device_to_host(const FPTYPE *device, FPTYPE *host, const int size)

Template Function deepmd::memcpy_device_to_host(FPTYPE *, std::vector<FPTYPE>&)

Defined in File gpu_rocm.h

Function Documentation

template<typename FPTYPE> void deepmd::memcpy_device_to_host(FPTYPE *device, std::vector<FPTYPE> &host)

Template Function deepmd::memcpy_host_to_device(FPTYPE *, const std::vector<FPTYPE>&)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::memcpy_host_to_device(FPTYPE *device, const std::vector<FPTYPE> &host)

Template Function deepmd::memcpy_host_to_device(FPTYPE , const FPTYPE , const int)

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::memcpy_host_to_device(FPTYPE *device, const FPTYPE *host, const int size)

Template Function deepmd::memcpy_host_to_device(FPTYPE *, std::vector<FPTYPE>&)

Defined in File gpu_rocm.h

Function Documentation

template<typename FPTYPE> void deepmd::memcpy_host_to_device(FPTYPE *device, std::vector<FPTYPE> &host)

Template Function deepmd::memset_device_memory

Defined in File gpu_cuda.h

Function Documentation

template<typename FPTYPE> void deepmd::memset_device_memory(FPTYPE *device, const int var, const int size)

Template Function deepmd::normalize_coord_cpu

Defined in File coord.h

Function Documentation

template<typename FPTYPE> void deepmd::normalize_coord_cpu(FPTYPE *coord, const int natom, const deepmd::Region<FPTYPE> &region)

Template Function deepmd::normalize_coord_gpu

Defined in File coord.h

Function Documentation

template<typename FPTYPE> void deepmd::normalize_coord_gpu(FPTYPE *coord, const int natom, const deepmd::Region<FPTYPE> &region)

Template Function deepmd::pair_tab_cpu

Defined in File pair_tab.h

Function Documentation

template<typename FPTYPE> void deepmd::pair_tab_cpu(FPTYPE *energy, FPTYPE *force, FPTYPE *virial, const double *table_info, const double *table_data, const FPTYPE *rij, const FPTYPE *scale, const int *type, const int *nlist, const int *natoms, const std::vector<int> &sel_a, const std::vector<int> &sel_r)

Template Function deepmd::prod_env_mat_a_cpu

Defined in File prod_env_mat.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_env_mat_a_cpu(FPTYPE *em, FPTYPE *em_deriv, FPTYPE *rij, int *nlist, const FPTYPE *coord, const int *type, const InputNlist &inlist, const int max_nbor_size, const FPTYPE *avg, const FPTYPE *std, const int nloc, const int nall, const float rcut, const float rcut_smth, const std::vector<int> sec, const int *f_type = NULL)

Template Function deepmd::prod_env_mat_a_gpu_cuda

Defined in File prod_env_mat.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_env_mat_a_gpu_cuda(FPTYPE *em, FPTYPE *em_deriv, FPTYPE *rij, int *nlist, const FPTYPE *coord, const int *type, const InputNlist &gpu_inlist, int *array_int, unsigned long long *array_longlong, const int max_nbor_size, const FPTYPE *avg, const FPTYPE *std, const int nloc, const int nall, const float rcut, const float rcut_smth, const std::vector<int> sec, const int *f_type = NULL)

Template Function deepmd::prod_env_mat_a_nvnmd_quantize_cpu

Defined in File prod_env_mat_nvnmd.h

Function Documentation

Warning

doxygenfunction: Unable to resolve function “deepmd::prod_env_mat_a_nvnmd_quantize_cpu” with arguments (FPTYPE*, FPTYPE*, FPTYPE*, int*, const FPTYPE*, const int*, const InputNlist&, const int, const FPTYPE*, const FPTYPE*, const int, const int, const float, const float, const std::vector<int>, const FPTYPE) in doxygen xml output for project “core” from directory: _build/core/xml/. Potential matches:

- template<typename FPTYPE> void prod_env_mat_a_nvnmd_quantize_cpu(FPTYPE *em, FPTYPE *em_deriv, FPTYPE *rij, int *nlist, const FPTYPE *coord, const int *type, const InputNlist &inlist, const int max_nbor_size, const FPTYPE *avg, const FPTYPE *std, const int nloc, const int nall, const float rcut, const float rcut_smth, const std::vector<int> sec, const FPTYPE precs[3])

Template Function deepmd::prod_env_mat_r_cpu

Defined in File prod_env_mat.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_env_mat_r_cpu(FPTYPE *em, FPTYPE *em_deriv, FPTYPE *rij, int *nlist, const FPTYPE *coord, const int *type, const InputNlist &inlist, const int max_nbor_size, const FPTYPE *avg, const FPTYPE *std, const int nloc, const int nall, const float rcut, const float rcut_smth, const std::vector<int> sec)

Template Function deepmd::prod_env_mat_r_gpu_cuda

Defined in File prod_env_mat.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_env_mat_r_gpu_cuda(FPTYPE *em, FPTYPE *em_deriv, FPTYPE *rij, int *nlist, const FPTYPE *coord, const int *type, const InputNlist &gpu_inlist, int *array_int, unsigned long long *array_longlong, const int max_nbor_size, const FPTYPE *avg, const FPTYPE *std, const int nloc, const int nall, const float rcut, const float rcut_smth, const std::vector<int> sec)

Template Function deepmd::prod_force_a_cpu

Defined in File prod_force.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_a_cpu(FPTYPE *force, const FPTYPE *net_deriv, const FPTYPE *in_deriv, const int *nlist, const int nloc, const int nall, const int nnei, const int start_index = 0)

Template Function deepmd::prod_force_a_gpu_cuda

Defined in File prod_force.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_a_gpu_cuda(FPTYPE *force, const FPTYPE *net_deriv, const FPTYPE *in_deriv, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::prod_force_grad_a_cpu

Defined in File prod_force_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_grad_a_cpu(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_force_grad_a_gpu_cuda

Defined in File prod_force_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_grad_a_gpu_cuda(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_force_grad_r_cpu

Defined in File prod_force_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_grad_r_cpu(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_force_grad_r_gpu_cuda

Defined in File prod_force_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_grad_r_gpu_cuda(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_force_r_cpu

Defined in File prod_force.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_r_cpu(FPTYPE *force, const FPTYPE *net_deriv, const FPTYPE *in_deriv, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::prod_force_r_gpu_cuda

Defined in File prod_force.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_force_r_gpu_cuda(FPTYPE *force, const FPTYPE *net_deriv, const FPTYPE *in_deriv, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::prod_virial_a_cpu

Defined in File prod_virial.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_a_cpu(FPTYPE *virial, FPTYPE *atom_virial, const FPTYPE *net_deriv, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::prod_virial_a_gpu_cuda

Defined in File prod_virial.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_a_gpu_cuda(FPTYPE *virial, FPTYPE *atom_virial, const FPTYPE *net_deriv, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::prod_virial_grad_a_cpu

Defined in File prod_virial_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_grad_a_cpu(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_virial_grad_a_gpu_cuda

Defined in File prod_virial_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_grad_a_gpu_cuda(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_virial_grad_r_cpu

Defined in File prod_virial_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_grad_r_cpu(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_virial_grad_r_gpu_cuda

Defined in File prod_virial_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_grad_r_gpu_cuda(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::prod_virial_r_cpu

Defined in File prod_virial.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_r_cpu(FPTYPE *virial, FPTYPE *atom_virial, const FPTYPE *net_deriv, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::prod_virial_r_gpu_cuda

Defined in File prod_virial.h

Function Documentation

template<typename FPTYPE> void deepmd::prod_virial_r_gpu_cuda(FPTYPE *virial, FPTYPE *atom_virial, const FPTYPE *net_deriv, const FPTYPE *env_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::soft_min_switch_cpu

Defined in File soft_min_switch.h

Function Documentation

template<typename FPTYPE> void deepmd::soft_min_switch_cpu(FPTYPE *sw_value, FPTYPE *sw_deriv, const FPTYPE *rij, const int *nlist, const int &nloc, const int &nnei, const FPTYPE &alpha, const FPTYPE &rmin, const FPTYPE &rmax)

Template Function deepmd::soft_min_switch_force_cpu

Defined in File soft_min_switch_force.h

Function Documentation

template<typename FPTYPE> void deepmd::soft_min_switch_force_cpu(FPTYPE *force, const FPTYPE *du, const FPTYPE *sw_deriv, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::soft_min_switch_force_grad_cpu

Defined in File soft_min_switch_force_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::soft_min_switch_force_grad_cpu(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *sw_deriv, const int *nlist, const int nloc, const int nnei)

Template Function deepmd::soft_min_switch_virial_cpu

Defined in File soft_min_switch_virial.h

Function Documentation

template<typename FPTYPE> void deepmd::soft_min_switch_virial_cpu(FPTYPE *virial, FPTYPE *atom_virial, const FPTYPE *du, const FPTYPE *sw_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nall, const int nnei)

Template Function deepmd::soft_min_switch_virial_grad_cpu

Defined in File soft_min_switch_virial_grad.h

Function Documentation

template<typename FPTYPE> void deepmd::soft_min_switch_virial_grad_cpu(FPTYPE *grad_net, const FPTYPE *grad, const FPTYPE *sw_deriv, const FPTYPE *rij, const int *nlist, const int nloc, const int nnei)

Function deepmd::spline3_switch

Defined in File switcher.h

Function Documentation

inline void deepmd::spline3_switch(double &vv, double &dd, const double &xx, const double &rmin, const double &rmax)

Template Function deepmd::spline5_switch

Defined in File switcher.h

Function Documentation

template<typename FPTYPE> inline void deepmd::spline5_switch(FPTYPE &vv, FPTYPE &dd, const FPTYPE &xx, const float &rmin, const float &rmax)

Template Function deepmd::tabulate_fusion_se_a_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_a_cpu(FPTYPE *out, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_a_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_a_gpu_cuda(FPTYPE *out, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_a_grad_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_a_grad_cpu(FPTYPE *dy_dem_x, FPTYPE *dy_dem, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dy, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_a_grad_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_a_grad_gpu_cuda(FPTYPE *dy_dem_x, FPTYPE *dy_dem, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dy, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_a_grad_grad_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_a_grad_grad_cpu(FPTYPE *dz_dy, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dz_dy_dem_x, const FPTYPE *dz_dy_dem, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_a_grad_grad_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_a_grad_grad_gpu_cuda(FPTYPE *dz_dy, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dz_dy_dem_x, const FPTYPE *dz_dy_dem, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_r_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_r_cpu(FPTYPE *out, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_r_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_r_gpu_cuda(FPTYPE *out, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_r_grad_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_r_grad_cpu(FPTYPE *dy_dem, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em, const FPTYPE *dy, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_r_grad_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_r_grad_gpu_cuda(FPTYPE *dy_dem, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em, const FPTYPE *dy, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_r_grad_grad_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_r_grad_grad_cpu(FPTYPE *dz_dy, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em, const FPTYPE *dz_dy_dem, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_r_grad_grad_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_r_grad_grad_gpu_cuda(FPTYPE *dz_dy, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em, const FPTYPE *dz_dy_dem, const int nloc, const int nnei, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_t_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_t_cpu(FPTYPE *out, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const int nloc, const int nnei_i, const int nnei_j, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_t_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_t_gpu_cuda(FPTYPE *out, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const int nloc, const int nnei_i, const int nnei_j, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_t_grad_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_t_grad_cpu(FPTYPE *dy_dem_x, FPTYPE *dy_dem, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dy, const int nloc, const int nnei_i, const int nnei_j, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_t_grad_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_t_grad_gpu_cuda(FPTYPE *dy_dem_x, FPTYPE *dy_dem, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dy, const int nloc, const int nnei_i, const int nnei_j, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_t_grad_grad_cpu

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_t_grad_grad_cpu(FPTYPE *dz_dy, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dz_dy_dem_x, const FPTYPE *dz_dy_dem, const int nloc, const int nnei_i, const int nnei_j, const int last_layer_size)

Template Function deepmd::tabulate_fusion_se_t_grad_grad_gpu_cuda

Defined in File tabulate.h

Function Documentation

template<typename FPTYPE> void deepmd::tabulate_fusion_se_t_grad_grad_gpu_cuda(FPTYPE *dz_dy, const FPTYPE *table, const FPTYPE *table_info, const FPTYPE *em_x, const FPTYPE *em, const FPTYPE *dz_dy_dem_x, const FPTYPE *dz_dy_dem, const int nloc, const int nnei_i, const int nnei_j, const int last_layer_size)

Template Function deepmd::test_encoding_decoding_nbor_info_gpu_cuda

Defined in File fmt_nlist.h

Function Documentation

template<typename FPTYPE> void deepmd::test_encoding_decoding_nbor_info_gpu_cuda(uint_64 *key, int *out_type, int *out_index, const int *in_type, const FPTYPE *in_dist, const int *in_index, const int size_of_array)

Function deepmd::use_nei_info_cpu

Defined in File neighbor_list.h

Function Documentation

void deepmd::use_nei_info_cpu(int *nlist, int *ntype, bool *nmask, const int *type, const int *nlist_map, const int nloc, const int nnei, const int ntypes, const bool b_nlist_map)

Function deepmd::use_nei_info_gpu

Defined in File neighbor_list.h

Function Documentation

void deepmd::use_nei_info_gpu(int *nlist, int *ntype, bool *nmask, const int *type, const int *nlist_map, const int nloc, const int nnei, const int ntypes, const bool b_nlist_map)

Function deepmd::use_nlist_map

Defined in File neighbor_list.h

Function Documentation

void deepmd::use_nlist_map(int *nlist, const int *nlist_map, const int nloc, const int nnei)

Template Function deepmd::volume_cpu

Defined in File region.h

Function Documentation

template<typename FPTYPE> FPTYPE deepmd::volume_cpu(const Region<FPTYPE> &region)

Template Function deepmd::volume_gpu

Defined in File region.h

Function Documentation

template<typename FPTYPE> void deepmd::volume_gpu(FPTYPE *volume, const Region<FPTYPE> &region)

Function DPAssert(cudaError_t, const char *, int, bool)

Defined in File gpu_cuda.h

Function Documentation

inline void DPAssert(cudaError_t code, const char *file, int line, bool abort = true)

Function DPAssert(hipError_t, const char *, int, bool)

Defined in File gpu_rocm.h

Function Documentation

inline void DPAssert(hipError_t code, const char *file, int line, bool abort = true)

Function env_mat_a

Defined in File env_mat.h

Function Documentation

void env_mat_a(std::vector<double> &descrpt_a, std::vector<double> &descrpt_a_deriv, std::vector<double> &rij_a, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const int &i_idx, const std::vector<int> &fmt_nlist, const std::vector<int> &sec, const double &rmin, const double &rmax)

Function env_mat_r

Defined in File env_mat.h

Function Documentation

void env_mat_r(std::vector<double> &descrpt_r, std::vector<double> &descrpt_r_deriv, std::vector<double> &rij_r, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const int &i_idx, const std::vector<int> &fmt_nlist, const std::vector<int> &sec, const double &rmin, const double &rmax)

Template Function format_nlist_i_cpu

Defined in File fmt_nlist.h

Function Documentation

template<typename FPTYPE> int format_nlist_i_cpu(std::vector<int> &fmt_nei_idx_a, const std::vector<FPTYPE> &posi, const std::vector<int> &type, const int &i_idx, const std::vector<int> &nei_idx_a, const float &rcut, const std::vector<int> &sec_a)

Function format_nlist_i_fill_a

Defined in File fmt_nlist.h

Function Documentation

int format_nlist_i_fill_a(std::vector<int> &fmt_nei_idx_a, std::vector<int> &fmt_nei_idx_r, const std::vector<double> &posi, const int &ntypes, const std::vector<int> &type, const SimulationRegion<double> &region, const bool &b_pbc, const int &i_idx, const std::vector<int> &nei_idx_a, const std::vector<int> &nei_idx_r, const double &rcut, const std::vector<int> &sec_a, const std::vector<int> &sec_r)

Function nborAssert(cudaError_t, const char *, int, bool)

Defined in File gpu_cuda.h

Function Documentation

inline void nborAssert(cudaError_t code, const char *file, int line, bool abort = true)

Function nborAssert(hipError_t, const char *, int, bool)

Defined in File gpu_rocm.h

Function Documentation

inline void nborAssert(hipError_t code, const char *file, int line, bool abort = true)

Function omp_get_num_threads

Defined in File ewald.h

Function Documentation

int omp_get_num_threads()

Function omp_get_thread_num

Defined in File ewald.h

Function Documentation

int omp_get_thread_num()

Variables

Variable deepmd::ElectrostaticConvertion

Defined in File ewald.h

Variable Documentation

const double deepmd::ElectrostaticConvertion = 14.39964535475696995031

Defines

Define DPErrcheck

Defined in File gpu_cuda.h

Define Documentation

DPErrcheck(res)

Define DPErrcheck

Defined in File gpu_rocm.h

Define Documentation

DPErrcheck(res)

Define GPU_MAX_NBOR_SIZE

Defined in File gpu_cuda.h

Define Documentation

GPU_MAX_NBOR_SIZE

Define GPU_MAX_NBOR_SIZE

Defined in File gpu_rocm.h

Define Documentation

GPU_MAX_NBOR_SIZE

Define MOASPNDIM

Defined in File SimulationRegion.h

Define Documentation

MOASPNDIM

Define nborErrcheck

Defined in File gpu_cuda.h

Define Documentation

nborErrcheck(res)

Define nborErrcheck

Defined in File gpu_rocm.h

Define Documentation

nborErrcheck(res)

Define SQRT_2_PI

Defined in File device.h

Define Documentation

SQRT_2_PI

Define TPB

Defined in File device.h

Define Documentation

TPB

Typedefs

Typedef int_64

Defined in File device.h

Typedef Documentation

typedef long long int_64

Typedef uint_64

Defined in File device.h

Typedef Documentation

typedef unsigned long long uint_64

License

The project DeePMD-kit is licensed under GNU LGPLv3.0.

Authors and Credits

Package Contributors

AnguseZhang
Chenxing Luo
Chun Cai
Davide Tisi
Denghui Lu
Duo
GeiduanLiu
Han Wang
Jia-Xin Zhu
Jiequn Han
Jingchao Zhang
Jinzhe Zeng
Linfeng Zhang
LiuGroupHNU
Lu
Marián Rynik
Nick Lin
Rhys Goodall
Shaochen Shi
Xia, Yu
YWolfeee
Ye Ding
Yifan Li李一帆
Yingze Wang
Yixiao Chen
Zeyu Li
ZhengdQin
ZiyaoLi
baohan
bwang-ecnu
deepmodeling
denghuilu
haidi
hlyang
hsulab
iProzd
jxxiaoshaoye
liangadam
likefallwind
marian-code
njzjz
pkulzy
readthedocs-assistant
tuoping
wsyxbcl
ziyao

Other Credits

Zhang ZiXuan for designing the Deepmodeling logo.
Everyone on the Deepmodeling mailing list for contributing to many discussions and decisions!

DeePMD-kit’s documentation

Getting Started

Easy install

Install off-line packages

Install with conda

Install with docker

Prepare data with dpdata

Train a model

Freeze a model

Test a model

Run MD with LAMMPS

Installation

Easy install

Install off-line packages

Install with conda

Install with docker

Install from source code

Install the python interface

Install the Tensorflow’s python interface

Install the DeePMD-kit’s python interface

Install horovod and mpi4py

Install the C++ interface

Install the Tensorflow’s C++ interface

Install the DeePMD-kit’s C++ interface

Install LAMMPS

Install LAMMPS’s DeePMD-kit module (built-in mode)

Install LAMMPS (plugin mode)

Install i-PI

Install GROMACS with DeepMD

Patch source code of GROMACS

Compile GROMACS with deepmd-kit

Building conda packages

Data

System

Formats of a system

NumPy format

HDF5 format

Raw format and data conversion

Prepare data with dpdata

Model

Overall

Descriptor "se_e2_a"

Descriptor "se_e2_r"

Descriptor "se_e3"

Descriptor "se_atten"

DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation

Installation

Introduction to new features of DPA-1

Descriptor "se_atten"

Fitting "ener"

Type embedding

Type map

Data format

Training example

Descriptor "hybrid"

Determine sel

Fit energy

The fitting network

Loss

Fit tensor like Dipole and Polarizability

The fitting Network

Loss

Training Data Preparation

Train the Model

Type embedding approach

Type embedding net

Deep potential long-range (DPLR)

Train a deep Wannier model for Wannier centroids

Train the DPLR model

Molecular dynamics simulation with DPLR

Deep Potential - Range Correction (DPRc)

Training data

Training the DPRc model

Run MD simulations

Training

Train a model

Advanced options

Learning rate

Training parameters

Options and environment variables

Descriptor `"se_e2_a"`

Descriptor `"se_e2_r"`

Descriptor `"se_e3"`

Descriptor `"se_atten"`

Descriptor `"se_atten"`

Fitting `"ener"`

Descriptor `"hybrid"`

Determine `sel`

Fit `tensor` like `Dipole` and `Polarizability`

Adjust `sel` of a frozen model

pair_style `deepmd`