DeePMD-kit’s documentation

Download and install

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

Easy installation methods

There 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 run LAMMPS in parallel.

Offline packages

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

With conda

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

To install the CPU version:

conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

With Docker

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

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu

To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu

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 the tensorflow’s Python interface. The full instruction can be found on the tensorflow’s official website. 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==2.3.0

It is highly recommanded to keep the consistency of the TensorFlow version for the python and C++ interfaces. 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==2.3.0

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.

Install the DeePMD-kit’s python interface

Execute

pip install deepmd-kit

To test the installation, one may 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 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

Check the compiler version on your machine

gcc --version

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 >= 4.9.

First the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be in consistent with the python interface. We assume that you have followed our instruction and installed tensorflow python interface 1.14.0 with, then you may follow the instruction for CPU to install the corresponding C++ interface (CPU only). If one wants GPU supports, he/she should follow the instruction for GPU to install the C++ interface.

Install the DeePMD-kit’s C++ interface

Clone the DeePMD-kit source code

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

Now goto 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. The DeePMD-kit will automatically detect if a CUDA tool-kit is available on your machine and build the GPU support accordingly. If you want to force the cmake to find CUDA tool-kit, you can speicify the key USE_CUDA_TOOLKIT,

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

and you may further asked to provide CUDA_TOOLKIT_ROOT_DIR. If the cmake has executed successfully, then

make
make install

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
$ ls $deepmd_root/lib
libdeepmd_ipi.so  libdeepmd_op.so  libdeepmd.so

Install LAMMPS’s DeePMD-kit module

DeePMD-kit provide 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. Now download your favorite LAMMPS code, and uncompress it (I assume that you have downloaded the tar lammps-stable.tar.gz)

cd /some/workspace
tar xf lammps-stable.tar.gz

The source code of LAMMPS is stored in directory, for example lammps-31Mar17. Now go into the LAMMPS code and copy the DeePMD-kit module like this

cd lammps-31Mar17/src/
cp -r $deepmd_source_dir/source/build/USER-DEEPMD .

Now build LAMMPS

make yes-user-deepmd
make mpi -j4

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 end up with an executable lmp_mpi.

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

make no-user-deepmd

Use DeePMD-kit

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

  1. Prepare data
  2. Train a model
  3. Freeze the model
  4. Test the model
  5. Inference with the model

Prepare data

One needs to provide the following information to train a model: the atom type, the simulation box, the atom coordinate, the atom force, system energy and virial. A snapshot of a system that contains these information is called a frame. We use the following convention of units:

Property| Unit — | :—: Time | ps Length | Å Energy | eV Force | eV/Å Pressure| Bar

The frames of the system are stored in two formats. A raw file is a plain text file with each information item written in one file and one frame written on one line. The default files that provide box, coordinate, force, energy and virial are box.raw, coord.raw, force.raw, energy.raw and virial.raw, respectively. We recommend you use these file names. 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. The coordinate file coord.raw is organized similarly. In box.raw, the 9 components of the box vectors should be provided on each line. In virial.raw, the 9 components of the virial tensor should be provided on each line. The number of lines of all raw files should be identical.

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

The second format is the data sets of numpy binary data that are directly used by the training program. User can use the script $deepmd_source_dir/data/raw/raw_to_set.sh to convert the prepared raw files to data sets. 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. The last set (set.002) is used as testing set, while the rest sets (set.000 and set.001) are used as training sets. One do not need to take care of the binary data files in each of the set.* directories. The path containing set.* and type.raw is called a system.

Train a model

Write the input script

The method of training is explained in our [DeePMD][2] and [DeepPot-SE][3] papers. With the source code we provide a small training dataset taken from 400 frames generated by NVT ab-initio water MD trajectory with 300 frames for training and 100 for testing. An example training parameter file is provided. One can try with the training by

$ cd $deepmd_source_dir/examples/water/train/
$ dp train water_se_a.json

where water_se_a.json is the json format parameter file that controls the training. It is also possible to use yaml format file with the same keys as json (see water_se_a.yaml example). You can use script json2yaml.py in data/json/ dir to convert your json files to yaml. The components of the water.json contains four parts, model, learning_rate, loss and training.

The model section specify how the deep potential model is built. An example of the smooth-edition is provided as follows

    "model": {
	"type_map":	["O", "H"],
	"descriptor" :{
	    "type":		"se_a",
	    "rcut_smth":	5.80,
	    "rcut":		6.00,
	    "sel":		[46, 92],
	    "neuron":		[25, 50, 100],
	    "axis_neuron":	16,
	    "resnet_dt":	false,
	    "seed":		1,
	    "_comment":		" that's all"
	},
	"fitting_net" : {
	    "neuron":		[240, 240, 240],
	    "resnet_dt":	true,	    
	    "seed":		1,
	    "_comment":		" that's all"
	},
	"_comment":	" that's all"
    }

The type_map is optional, which provide the element names (but not restricted to) for corresponding atom types.

The construction of the descriptor is given by option descriptor. The type of the descriptor is set to "se_a", which means smooth-edition, angular infomation. The 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 layers from input end to the output end, respectively. The axis_neuron specifies the size of submatrix of the embedding matrix, the axis matrix as explained in the [DeepPot-SE paper][3]. 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 build between them. If the option resnet_dt is set 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.

The construction of the fitting net is give by fitting_net. The key neuron specifies the size of the fitting net. If two neighboring layers are of the same size, then a ResNet architecture is build between them. If the option resnet_dt is set 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.

An example of the learning_rate is given as follows

    "learning_rate" :{
	"type":		"exp",
	"start_lr":	0.005,
	"decay_steps":	5000,
	"decay_rate":	0.95,
	"_comment":	"that's all"
    }

The option start_lr, decay_rate and decay_steps specify how the learning rate changes. For example, the tth batch will be trained with learning rate:

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

An example of the loss 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,
	"_comment":	" that's all"
    }

The options start_pref_e, limit_pref_e, start_pref_f, limit_pref_f, start_pref_v and limit_pref_v determine how the prefactors of energy error, force error and virial error changes in the loss function (see the appendix of the [DeePMD paper][2] for details). Taking the prefactor of force error for example, the prefactor at batch t is

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

Since we do not have virial data, the virial prefactors start_pref_v and limit_pref_v are set to 0.

An example of training is

    "training" : {
	"systems":	["../data1/", "../data2/"],
	"set_prefix":	"set",    
	"stop_batch":	1000000,
	"_comment": " batch_size can be supplied with, e.g. 1, or auto (string) or [10, 20]",
	"batch_size":	1,

	"seed":		1,

	"_comment": " display and restart",
	"_comment": " frequencies counted in batch",
	"disp_file":	"lcurve.out",
	"disp_freq":	100,
	"_comment": " numb_test can be supplied with, e.g. 1, or XX% (string) or [10, 20]",
	"numb_test":	10,
	"save_freq":	1000,
	"save_ckpt":	"model.ckpt",
	"load_ckpt":	"model.ckpt",
	"disp_training":true,
	"time_training":true,
	"profiling":	false,
	"profiling_file":"timeline.json",
	"_comment":	"that's all"
    }

The option systems provide location of the systems (path to set.* and type.raw). It is a vector, thus DeePMD-kit allows you to provide multiple systems. DeePMD-kit will train the model with the systems in the vector one by one in a cyclic manner. It is warned that the example water data (in folder examples/data/water) is of very limited amount, is provided only for testing purpose, and should not be used to train a productive model.

The option batch_size specifies the number of frames in each batch. It can be set to "auto" to enable a automatic batch size or it can be input as a list setting batch size individually for each system. The option stop_batch specifies the total number of batches will be used in the training.

The option numb_test specifies the number of tests that will be used for each system. If it is an integer each system will be tested with the same number of tests. It can be set to percentage "XX%" to use XX% of frames of each system for its testing or it can be input as a list setting numer of tests individually for each system (the order should correspond to ordering of the systems key in json).

Training

The training can be invoked by

$ dp train water_se_a.json

During the training, the error of the model is tested every disp_freq batches with numb_test frames from the last set in the systems directory on the fly, and the results are output to disp_file. A typical disp_file looks like

# batch      l2_tst    l2_trn    l2_e_tst  l2_e_trn    l2_f_tst  l2_f_trn         lr
      0    2.67e+01  2.57e+01    2.21e-01  2.22e-01    8.44e-01  8.12e-01    1.0e-03
    100    6.14e+00  5.40e+00    3.01e-01  2.99e-01    1.93e-01  1.70e-01    1.0e-03
    200    5.02e+00  4.49e+00    1.53e-01  1.53e-01    1.58e-01  1.42e-01    1.0e-03
    300    4.36e+00  3.71e+00    7.32e-02  7.27e-02    1.38e-01  1.17e-01    1.0e-03
    400    4.04e+00  3.29e+00    3.16e-02  3.22e-02    1.28e-01  1.04e-01    1.0e-03

The first column displays the number of batches. The second and third columns display the loss function evaluated by numb_test frames randomly chosen from the test set and that evaluated by the current training batch, respectively. The fourth and fifth columns display the RMS energy error (normalized by number of atoms) evaluated by numb_test frames randomly chosen from the test set and that evaluated by the current training batch, respectively. The sixth and seventh columns display the RMS force error (component-wise) evaluated by numb_test frames randomly chosen from the test set and that evaluated by the current training batch, respectively. The last column displays the current learning rate.

Checkpoints will be written to files with prefix save_ckpt every save_freq batches. If restart is set to true, then the training will start from the checkpoint named load_ckpt, rather than from scratch.

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

The keys intra_op_parallelism_threads and inter_op_parallelism_threads are Tensorflow configurations for multithreading, which are explained here. Skipping -t and OMP_NUM_THREADS leads to the default setting of these keys in the Tensorflow.

--init-model model.ckpt, for example, 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.

On some resources limited machines, one may want to 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=6
export TF_INTRA_OP_PARALLELISM_THREADS=3
export TF_INTER_OP_PARALLELISM_THREADS=2
dp train input.json

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

Model inference

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

import deepmd.DeepPot as DP
import numpy as np
dp = DP('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.

Run MD with LAMMPS

Include deepmd in the pair style

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. The pair_coeff should be left blank. 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. A detailed documentation of this pair style is available..

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.

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 that computes the interactions (including energy, force and virial). The server and client communicates via the Unix domain socket or the Internet socket. The client can be started by

$ 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 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.

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())

Training parameters

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.

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.

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.

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

The type of the descritpor. Valid types are loc_frame, se_a, se_r and se_ar.

  • loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.
  • se_a: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.
  • se_r: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.
  • se_ar: A hybrid of se_a and se_r. Typically se_a has a smaller cut-off while the se_r has a larger cut-off.

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_a:

sel:
type: list
argument path: model/descriptor[se_a]/sel

A list of integers. 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.

rcut:
type: float, optional, default: 6.0
argument path: model/descriptor[se_a]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model/descriptor[se_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_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
argument path: model/descriptor[se_a]/axis_neuron

Size of the submatrix of G (embedding matrix).

activation_function:
type: str, optional, default: tanh
argument path: model/descriptor[se_a]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”.

resnet_dt:
type: bool, optional, default: False
argument path: model/descriptor[se_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_a]/type_one_side

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

precision:
type: str, optional, default: float64
argument path: model/descriptor[se_a]/precision

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

trainable:
type: bool, optional, default: True
argument path: model/descriptor[se_a]/trainable

If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model/descriptor[se_a]/seed

Random seed for parameter initialization

exclude_types:
type: list, optional, default: []
argument path: model/descriptor[se_a]/exclude_types

The Excluded types

set_davg_zero:
type: bool, optional, default: False
argument path: model/descriptor[se_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_r:

sel:
type: list
argument path: model/descriptor[se_r]/sel

A list of integers. 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.

rcut:
type: float, optional, default: 6.0
argument path: model/descriptor[se_r]/rcut

The cut-off radius.

rcut_smth:
type: float, optional, default: 0.5
argument path: model/descriptor[se_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_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_r]/activation_function

The activation function in the embedding net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”.

resnet_dt:
type: bool, optional, default: False
argument path: model/descriptor[se_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_r]/type_one_side

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

precision:
type: str, optional, default: float64
argument path: model/descriptor[se_r]/precision

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

trainable:
type: bool, optional, default: True
argument path: model/descriptor[se_r]/trainable

If the parameters in the embedding net is trainable

seed:
type: int | NoneType, optional
argument path: model/descriptor[se_r]/seed

Random seed for parameter initialization

exclude_types:
type: list, optional, default: []
argument path: model/descriptor[se_r]/exclude_types

The Excluded types

set_davg_zero:
type: bool, optional, default: False
argument path: model/descriptor[se_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 se_ar:

a:
type: dict
argument path: model/descriptor[se_ar]/a

The parameters of descriptor se_a

r:
type: dict
argument path: model/descriptor[se_ar]/r

The parameters of descriptor se_r

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

The type of the fitting. Valid types are ener, dipole, polar and global_polar.

  • ener: Fit an energy model (potential energy surface).
  • dipole: Fit an atomic dipole model. Atomic dipole labels for all the selected atoms (see sel_type) should be provided by dipole.npy in each data system. The file has number of frames lines and 3 times of number of selected atoms columns.
  • polar: Fit an atomic polarizability model. Atomic polarizability labels for all the selected atoms (see sel_type) should be provided by polarizability.npy in each data system. The file has number of frames lines and 9 times of number of selected atoms columns.
  • global_polar: Fit a polarizability model. Polarizability labels should be provided by polarizability.npy in each data system. The file has number of frames lines and 9 columns.

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]
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”.

precision:
type: str, optional, default: float64
argument path: model/fitting_net[ener]/precision

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

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: bool | list, 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]
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”.

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: float64
argument path: model/fitting_net[dipole]/precision

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

sel_type:
type: int | NoneType | list, optional
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]
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”.

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: float64
argument path: model/fitting_net[polar]/precision

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

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: float | list, optional, default: 1.0
argument path: model/fitting_net[polar]/scale

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

diag_shift:
type: float | list, optional, default: 0.0
argument path: model/fitting_net[polar]/diag_shift

The diagonal part of the polarizability matrix will be shifted by diag_shift. The shift operation is carried out after scale.

sel_type:
type: int | NoneType | list, optional
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

When type is set to global_polar:

neuron:
type: list, optional, default: [120, 120, 120]
argument path: model/fitting_net[global_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[global_polar]/activation_function

The activation function in the fitting net. Supported activation functions are “relu”, “relu6”, “softplus”, “sigmoid”, “tanh”, “gelu”.

resnet_dt:
type: bool, optional, default: True
argument path: model/fitting_net[global_polar]/resnet_dt

Whether to use a “Timestep” in the skip connection

precision:
type: str, optional, default: float64
argument path: model/fitting_net[global_polar]/precision

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

fit_diag:
type: bool, optional, default: True
argument path: model/fitting_net[global_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: float | list, optional, default: 1.0
argument path: model/fitting_net[global_polar]/scale

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

diag_shift:
type: float | list, optional, default: 0.0
argument path: model/fitting_net[global_polar]/diag_shift

The diagonal part of the polarizability matrix will be shifted by diag_shift. The shift operation is carried out after scale.

sel_type:
type: int | NoneType | list, optional
argument path: model/fitting_net[global_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[global_polar]/seed

Random seed for parameter initialization of the fitting net

loss:
type: dict
argument path: loss

The definition of loss function. The type of the loss depends on the type of the fitting. For fitting type ener, the prefactors before energy, force, virial and atomic energy losses may be provided. For fitting type dipole, polar and global_polar, the loss may be an empty dict or unset.

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

type:
type: str (flag key), default: ener
argument path: loss/type

The type of the loss. For fitting type ener, the loss type should be set to ener or left unset. For tensorial fitting types dipole, polar and global_polar, the type should be left unset. .

When type is set to ener:

start_pref_e:
type: float | int, 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: float | int, 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: float | int, 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: float | int, 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: float | int, 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: float | int, 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: float | int, optional, default: 0.0
argument path: loss[ener]/start_pref_ae

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_ae:
type: float | int, optional, default: 0.0
argument path: loss[ener]/limit_pref_ae

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.

relative_f:
type: float | NoneType, 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.

learning_rate:
type: dict
argument path: learning_rate

The learning rate options

start_lr:
type: float, optional, default: 0.001
argument path: learning_rate/start_lr

The learning rate the start of the training.

stop_lr:
type: float, optional, default: 1e-08
argument path: learning_rate/stop_lr

The desired learning rate at the end of the training.

decay_steps:
type: int, optional, default: 5000
argument path: learning_rate/decay_steps

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

training:
type: dict
argument path: training

The training options

systems:
type: list | str
argument path: training/systems

The data systems. This key can be provided with a listthat 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/set_prefix

The prefix of the sets in the systems.

stop_batch:
type: int
argument path: training/stop_batch

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

batch_size:
type: int | list | str, optional, default: auto
argument path: training/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 uses the same batch size.
  • string “auto”: automatically determines the batch size os 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 os that the batch_size times the number of atoms in the system is no less than N.
seed:
type: int | NoneType, optional
argument path: training/seed

The random seed for training.

disp_file:
type: str, optional, default: lcueve.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.

numb_test:
type: int | list | str, optional, default: 1
argument path: training/numb_test

Number of frames used for the test during training.

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.

pair_style deepmd command

Syntax

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 the model deviation will be computed
  • 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

           |Df_i|
Ef_i = -------------
       |f_i| + level

where Df_i is the absolute model deviation of the force on atom i, |f_i| is the norm of the the force and level 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.
  • The atom_style of the system should be atomic.
  • When using the atomic key word of deepmd is set, one should not use this pair style with MPI parallelization.

DeePMD-kit API

class deepmd.Data.DataSets(sys_path, set_prefix, seed=None, shuffle_test=True)
check_batch_size(batch_size)
check_test_size(test_size)
get_batch(batch_size)

returned property prefector [4] in order: energy, force, virial, atom_ener

get_ener()
get_natoms()
get_natoms_2(ntypes)
get_natoms_vec(ntypes)
get_numb_set()
get_set(data, idx=None)
get_sys_numb_batch(batch_size)
get_test()

returned property prefector [4] in order: energy, force, virial, atom_ener

get_type_map()
load_batch_set(set_name)
load_data(set_name, data_name, shape, is_necessary=True)
load_energy(set_name, nframes, nvalues, energy_file, atom_energy_file)

return : coeff_ener, ener, coeff_atom_ener, atom_ener

load_set(set_name, shuffle=True)
load_test_set(set_name, shuffle_test)
load_type(sys_path)
load_type_map(sys_path)
numb_aparam()
numb_fparam()
reset_iter()
set_numb_batch(batch_size)
stats_energy()
class deepmd.Data.DeepmdData(sys_path, set_prefix='set', shuffle_test=True, type_map=None, modifier=None)
add(key, ndof, atomic=False, must=False, high_prec=False, type_sel=None, repeat=1)
avg(key)
check_batch_size(batch_size)
check_test_size(test_size)
get_atom_type()
get_batch(batch_size)
get_data_dict()
get_natoms()
get_natoms_vec(ntypes)
get_ntypes()
get_numb_batch(batch_size, set_idx)
get_numb_set()
get_sys_numb_batch(batch_size)
get_test(ntests=-1)
get_type_map()
reduce(key_out, key_in)
reset_get_batch()
class deepmd.DataModifier.DipoleChargeModifier(model_name, model_charge_map, sys_charge_map, ewald_h=1, ewald_beta=1)
build_fv_graph()
eval(coord, box, atype, eval_fv=True)
eval_fv(coords, cells, atom_types, ext_f)
modify_data(data)
class deepmd.DataSystem.DataSystem(systems, set_prefix, batch_size, test_size, rcut, run_opt=None)
check_type_map_consistency(type_map_list)
compute_energy_shift()
format_name_length(name, width)
get_batch(sys_idx=None, sys_weights=None, style='prob_sys_size')
get_batch_size()
get_nbatches()
get_nsystems()
get_ntypes()
get_sys(sys_idx)
get_test(sys_idx=None)
get_type_map()
numb_fparam()
print_summary(run_opt)
process_sys_weights(sys_weights)
class deepmd.DataSystem.DeepmdDataSystem(systems, batch_size, test_size, rcut, set_prefix='set', shuffle_test=True, type_map=None, modifier=None)
add(key, ndof, atomic=False, must=False, high_prec=False, type_sel=None, repeat=1)
add_dict(adict)
compute_energy_shift(rcond=0.001, key='energy')
get_batch(sys_idx=None, sys_probs=None, auto_prob_style='prob_sys_size')

Get a batch of data from the data system

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.
sys_probs: list of float
The probabilitis of systems to get the batch. Summation of positive elements of this list should be no greater than 1. Element of this list can be negative, the probability of the corresponding system is determined automatically by the number of batches in the system.
auto_prob_style: float

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.
get_batch_size()
get_data_dict()
get_nbatches()
get_nsystems()
get_ntypes()
get_sys(idx)
get_sys_ntest(sys_idx=None)

Get number of tests for the currently selected system, or one defined by sys_idx.

get_test(sys_idx=None, n_test=-1)
get_type_map()
print_summary(run_opt, sys_probs=None, auto_prob_style='prob_sys_size')
reduce(key_out, key_in)
class deepmd.DeepDipole.DeepDipole(model_file, load_prefix='load', default_tf_graph=False)
class deepmd.DeepEval.DeepEval(model_file, load_prefix='load', default_tf_graph=False)

common methods for DeepPot, DeepWFC, DeepPolar, …

make_natoms_vec(atom_types)
reverse_map(vec, imap)
sort_input(coord, atom_type, sel_atoms=None)
class deepmd.DeepEval.DeepTensor(model_file, variable_name, variable_dof, load_prefix='load', default_tf_graph=False)

Evaluates a tensor model

eval(coords, cells, atom_types, atomic=True)
get_ntypes()
get_rcut()
get_sel_type()
get_type_map()
class deepmd.DeepPolar.DeepGlobalPolar(model_file, default_tf_graph=False)
eval(coords, cells, atom_types)
class deepmd.DeepPolar.DeepPolar(model_file, default_tf_graph=False)
class deepmd.DeepPot.DeepPot(model_file, default_tf_graph=False)
eval(coords, cells, atom_types, fparam=None, aparam=None, atomic=False)
eval_inner(coords, cells, atom_types, fparam=None, aparam=None, atomic=False)
get_dim_aparam()
get_dim_fparam()
get_ntypes()
get_rcut()
get_type_map()
class deepmd.DeepWFC.DeepWFC(model_file, default_tf_graph=False)
class deepmd.DescrptLocFrame.DescrptLocFrame(jdata)
build(coord_, atype_, natoms, box_, mesh, suffix='', reuse=None)
compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh)
get_dim_out()
get_nlist()
get_ntypes()
get_rcut()
get_rot_mat()
prod_force_virial(atom_ener, natoms)
class deepmd.DescrptSeA.DescrptSeA(jdata)
build(coord_, atype_, natoms, box_, mesh, suffix='', reuse=None)
compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh)
get_dim_out()
get_dim_rot_mat_1()
get_nlist()
get_ntypes()
get_rcut()
get_rot_mat()
prod_force_virial(atom_ener, natoms)
class deepmd.DescrptSeAR.DescrptSeAR(jdata)
build(coord_, atype_, natoms, box, mesh, suffix='', reuse=None)
compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh)
get_dim_out()
get_nlist_a()
get_nlist_r()
get_ntypes()
get_rcut()
prod_force_virial(atom_ener, natoms)
class deepmd.DescrptSeR.DescrptSeR(jdata)
build(coord_, atype_, natoms, box_, mesh, suffix='', reuse=None)
compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh)
get_dim_out()
get_nlist()
get_ntypes()
get_rcut()
prod_force_virial(atom_ener, natoms)
class deepmd.EwaldRecp.EwaldRecp(hh, beta)
eval(coord, charge, box)
class deepmd.Fitting.DipoleFittingSeA(jdata, descrpt)
build(input_d, rot_mat, natoms, reuse=None, suffix='')
get_out_size()
get_sel_type()
class deepmd.Fitting.EnerFitting(jdata, descrpt)
build(inputs, input_dict, natoms, reuse=None, suffix='')
compute_input_stats(all_stat, protection)
compute_output_stats(all_stat)
get_numb_aparam()
get_numb_fparam()
class deepmd.Fitting.GlobalPolarFittingSeA(jdata, descrpt)
build(input_d, rot_mat, natoms, reuse=None, suffix='')
get_out_size()
get_sel_type()
class deepmd.Fitting.PolarFittingLocFrame(jdata, descrpt)
build(input_d, rot_mat, natoms, reuse=None, suffix='')
get_out_size()
get_sel_type()
class deepmd.Fitting.PolarFittingSeA(jdata, descrpt)
build(input_d, rot_mat, natoms, reuse=None, suffix='')
compute_input_stats(all_stat, protection=0.01)
get_out_size()
get_sel_type()
class deepmd.Fitting.WFCFitting(jdata, descrpt)
build(input_d, rot_mat, natoms, reuse=None, suffix='')
get_out_size()
get_sel_type()
get_wfc_numb()
class deepmd.LearningRate.LearningRateExp(jdata)
build(global_step, stop_batch=None)
start_lr()
value(batch)
class deepmd.Loss.EnerDipoleLoss(jdata, **kwarg)
build(learning_rate, natoms, model_dict, label_dict, suffix)
print_header()
print_on_training(sess, natoms, feed_dict_test, feed_dict_batch)
class deepmd.Loss.EnerStdLoss(jdata, **kwarg)
build(learning_rate, natoms, model_dict, label_dict, suffix)
print_header()
print_on_training(sess, natoms, feed_dict_test, feed_dict_batch)
class deepmd.Loss.TensorLoss(jdata, **kwarg)
build(learning_rate, natoms, model_dict, label_dict, suffix)
print_header()
print_on_training(sess, natoms, feed_dict_test, feed_dict_batch)
class deepmd.Model.DipoleModel(jdata, descrpt, fitting)
class deepmd.Model.GlobalPolarModel(jdata, descrpt, fitting)
class deepmd.Model.Model(jdata, descrpt, fitting)
build(coord_, atype_, natoms, box, mesh, input_dict, suffix='', reuse=None)
data_stat(data)
get_ntypes()
get_rcut()
get_type_map()
model_type = 'ener'
class deepmd.Model.PolarModel(jdata, descrpt, fitting)
class deepmd.Model.TensorModel(jdata, descrpt, fitting, var_name)
build(coord_, atype_, natoms, box, mesh, input_dict, suffix='', reuse=None)
data_stat(data)
get_ntypes()
get_out_size()
get_rcut()
get_sel_type()
get_type_map()
class deepmd.Model.WFCModel(jdata, descrpt, fitting)
deepmd.Model.make_all_stat(data, nbatches, merge_sys=True)

pack data for statistics Parameters ———- data:

The data
merge_sys: bool (True)
Merge system data
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.merge_sys_stat(all_stat)
deepmd.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)
class deepmd.TabInter.TabInter(filename)
get()
reinit(filename)
class deepmd.Trainer.NNPTrainer(jdata, run_opt)
build(data, stop_batch=0)
get_global_step()
print_head()
test_on_the_fly(fp, data, feed_dict_batch)
train(data)

Indices and tables