dpgen simplify parameters

Note

One can load, modify, and export the input file by using our effective web-based tool DP-GUI online or hosted using the command line interface dpgen gui. All parameters below can be set in DP-GUI. By clicking “SAVE JSON”, one can download the input file.

simplify_jdata:

type: dict

argument path: simplify_jdata

Parameters for simplify.json, the first argument of dpgen simplify.

type_map:

type: list[str]

argument path: simplify_jdata/type_map

Atom types. Reminder: The elements in param.json, type.raw and data.lmp(when using lammps) should be in the same order.

mass_map:

type: str | list[float], optional, default: auto

argument path: simplify_jdata/mass_map

Standard atomic weights (default: “auto”). if one want to use isotopes, or non-standard element names, chemical symbols, or atomic number in the type_map list, please customize the mass_map list instead of using “auto”.

use_ele_temp:

type: int, optional, default: 0

argument path: simplify_jdata/use_ele_temp

Currently only support fp_style vasp.

0: no electron temperature.

1: eletron temperature as frame parameter.

2: electron temperature as atom parameter.

init_data_prefix:

type: str, optional

argument path: simplify_jdata/init_data_prefix

Prefix of initial data directories.

init_data_sys:

type: list[str]

argument path: simplify_jdata/init_data_sys

Paths of initial data. The path can be either a system diretory containing NumPy files or an HDF5 file. You may use either absolute or relative path here. Systems will be detected recursively in the directories or the HDF5 file.

sys_format:

type: str, optional, default: vasp/poscar

argument path: simplify_jdata/sys_format

Format of sys_configs.

init_batch_size:

type: str | list[typing.Union[int, str]], optional

argument path: simplify_jdata/init_batch_size

Each number is the batch_size of corresponding system for training in init_data_sys. One recommended rule for setting the sys_batch_size and init_batch_size is that batch_size mutiply number of atoms ot the stucture should be larger than 32. If set to auto, batch size will be 32 divided by number of atoms. This argument will not override the mixed batch size in default_training_param.

sys_configs_prefix:

type: str, optional

argument path: simplify_jdata/sys_configs_prefix

Prefix of sys_configs.

sys_configs:

type: list[list[str]]

argument path: simplify_jdata/sys_configs

2D list. Containing directories of structures to be explored in iterations for each system. Wildcard characters are supported here.

sys_batch_size:

type: list[typing.Union[int, str]], optional

argument path: simplify_jdata/sys_batch_size

Each number is the batch_size for training of corresponding system in sys_configs. If set to auto, batch size will be 32 divided by number of atoms. This argument will not override the mixed batch size in default_training_param.

labeled:

type: bool, optional, default: False

argument path: simplify_jdata/labeled

If true, the initial data is labeled.

pick_data:

type: str | list[str]

argument path: simplify_jdata/pick_data

(List of) Path to the directory with the pick data with the deepmd/npy or the HDF5 file with deepmd/hdf5 format. Systems are detected recursively.

init_pick_number:

type: int

argument path: simplify_jdata/init_pick_number

The number of initial pick data.

iter_pick_number:

type: int

argument path: simplify_jdata/iter_pick_number

The number of pick data in each iteration.

model_devi_f_trust_lo:

type: float

argument path: simplify_jdata/model_devi_f_trust_lo

The lower bound of forces for the selection for the model deviation.

model_devi_f_trust_hi:

type: float

argument path: simplify_jdata/model_devi_f_trust_hi

The higher bound of forces for the selection for the model deviation.

model_devi_e_trust_lo:

type: float, optional, default: 10000000000.0

argument path: simplify_jdata/model_devi_e_trust_lo

The lower bound of energy per atom for the selection for the model deviation. Requires DeePMD-kit version >=2.2.2.

model_devi_e_trust_hi:

type: float, optional, default: 10000000000.0

argument path: simplify_jdata/model_devi_e_trust_hi

The higher bound of energy per atom for the selection for the model deviation. Requires DeePMD-kit version >=2.2.2.

true_error_f_trust_lo:

type: float, optional, default: 10000000000.0

argument path: simplify_jdata/true_error_f_trust_lo

The lower bound of forces for the selection for the true error. Requires DeePMD-kit version >=2.2.4.

true_error_f_trust_hi:

type: float, optional, default: 10000000000.0

argument path: simplify_jdata/true_error_f_trust_hi

The higher bound of forces for the selection for the true error. Requires DeePMD-kit version >=2.2.4.

true_error_e_trust_lo:

type: float, optional, default: 10000000000.0

argument path: simplify_jdata/true_error_e_trust_lo

The lower bound of energy per atom for the selection for the true error. Requires DeePMD-kit version >=2.2.4.

true_error_e_trust_hi:

type: float, optional, default: 10000000000.0

argument path: simplify_jdata/true_error_e_trust_hi

The higher bound of energy per atom for the selection for the true error. Requires DeePMD-kit version >=2.2.4.

numb_models:

type: int

argument path: simplify_jdata/numb_models

Number of models to be trained in 00.train. 4 is recommend.

training_iter0_model_path:

type: list[str], optional

argument path: simplify_jdata/training_iter0_model_path

The model used to init the first iter training. Number of element should be equal to numb_models.

training_init_model:

type: bool, optional

argument path: simplify_jdata/training_init_model

Iteration > 0, the model parameters will be initilized from the model trained at the previous iteration. Iteration == 0, the model parameters will be initialized from training_iter0_model_path.

default_training_param:

type: dict

argument path: simplify_jdata/default_training_param

Training parameters for deepmd-kit in 00.train. You can find instructions from DeePMD-kit documentation.

dp_train_skip_neighbor_stat:

type: bool, optional, default: False

argument path: simplify_jdata/dp_train_skip_neighbor_stat

Append –skip-neighbor-stat flag to dp train.

dp_compress:

type: bool, optional, default: False

argument path: simplify_jdata/dp_compress

Use dp compress to compress the model.

training_reuse_iter:

type: int | NoneType, optional

argument path: simplify_jdata/training_reuse_iter

The minimal index of iteration that continues training models from old models of last iteration.

training_reuse_old_ratio:

type: str | float, optional, default: auto

argument path: simplify_jdata/training_reuse_old_ratio

The probability proportion of old data during training. It can be:

float: directly assign the probability of old data;

auto:f: automatic probability, where f is the new-to-old ratio;

auto: equivalent to auto:10.

This option is only adopted when continuing training models from old models. This option will override default parameters.

training_reuse_numb_steps:

type: int | NoneType, optional, default: None, alias: training_reuse_stop_batch

argument path: simplify_jdata/training_reuse_numb_steps

Number of training batch. This option is only adopted when continuing training models from old models. This option will override default parameters.

training_reuse_start_lr:

type: float | NoneType, optional, default: None

argument path: simplify_jdata/training_reuse_start_lr

The learning rate the start of the training. This option is only adopted when continuing training models from old models. This option will override default parameters.

training_reuse_start_pref_e:

type: int | float | NoneType, optional, default: None

argument path: simplify_jdata/training_reuse_start_pref_e

The prefactor of energy loss at the start of the training. This option is only adopted when continuing training models from old models. This option will override default parameters.

training_reuse_start_pref_f:

type: int | float | NoneType, optional, default: None

argument path: simplify_jdata/training_reuse_start_pref_f

The prefactor of force loss at the start of the training. This option is only adopted when continuing training models from old models. This option will override default parameters.

model_devi_activation_func:

type: NoneType | list[list[str]], optional

argument path: simplify_jdata/model_devi_activation_func

The activation function in the model. The shape of list should be (N_models, 2), where 2 represents the embedding and fitting network. This option will override default parameters.

srtab_file_path:

type: str, optional

argument path: simplify_jdata/srtab_file_path

The path of the table for the short-range pairwise interaction which is needed when using DP-ZBL potential

one_h5:

type: bool, optional, default: False

argument path: simplify_jdata/one_h5

When using DeePMD-kit, all of the input data will be merged into one HDF5 file.

training_init_frozen_model:

type: list[str], optional

argument path: simplify_jdata/training_init_frozen_model

At interation 0, initilize the model parameters from the given frozen models. Number of element should be equal to numb_models.

training_finetune_model:

type: list[str], optional

argument path: simplify_jdata/training_finetune_model

At interation 0, finetune the model parameters from the given frozen models. Number of element should be equal to numb_models.

fp_task_max:

type: int, optional

argument path: simplify_jdata/fp_task_max

Maximum of structures to be calculated in 02.fp of each iteration.

fp_task_min:

type: int, optional

argument path: simplify_jdata/fp_task_min

Minimum of structures to be calculated in 02.fp of each iteration.

fp_accurate_threshold:

type: float, optional

argument path: simplify_jdata/fp_accurate_threshold

If the accurate ratio is larger than this number, no fp calculation will be performed, i.e. fp_task_max = 0.

fp_accurate_soft_threshold:

type: float, optional

argument path: simplify_jdata/fp_accurate_soft_threshold

If the accurate ratio is between this number and fp_accurate_threshold, the fp_task_max linearly decays to zero.

ratio_failed:

type: float, optional

argument path: simplify_jdata/ratio_failed

Check the ratio of unsuccessfully terminated jobs. If too many FP tasks are not converged, RuntimeError will be raised.

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

fp_style:

type: str (flag key), default: none

argument path: simplify_jdata/fp_style

possible choices: none, vasp, gaussian, siesta, cp2k, abacus, pwmat, pwscf, custom

Software for First Principles, if labeled is false.

When fp_style is set to none:

No fp.

When fp_style is set to vasp:

VASP.

fp_pp_path:

type: str

argument path: simplify_jdata[vasp]/fp_pp_path

Directory of psuedo-potential file to be used for 02.fp exists.

fp_pp_files:

type: list[str]

argument path: simplify_jdata[vasp]/fp_pp_files

Psuedo-potential file to be used for 02.fp. Note that the order of elements should correspond to the order in type_map.

fp_incar:

type: str

argument path: simplify_jdata[vasp]/fp_incar

Input file for VASP. INCAR must specify KSPACING and KGAMMA.

fp_aniso_kspacing:

type: list[float], optional

argument path: simplify_jdata[vasp]/fp_aniso_kspacing

Set anisotropic kspacing. Usually useful for 1-D or 2-D materials. Only support VASP. If it is setting the KSPACING key in INCAR will be ignored.

cvasp:

type: bool, optional

argument path: simplify_jdata[vasp]/cvasp

If cvasp is true, DP-GEN will use Custodian to help control VASP calculation.

fp_skip_bad_box:

type: str, optional

argument path: simplify_jdata[vasp]/fp_skip_bad_box

Skip the configurations that are obviously unreasonable before 02.fp

When fp_style is set to gaussian:

Gaussian. The command should be set as g16 < input.

use_clusters:

type: bool, optional, default: False

argument path: simplify_jdata[gaussian]/use_clusters

If set to true, clusters will be taken instead of the whole system.

cluster_cutoff:

type: float, optional

argument path: simplify_jdata[gaussian]/cluster_cutoff

The soft cutoff radius of clusters if use_clusters is set to true. Molecules will be taken as whole even if part of atoms is out of the cluster. Use cluster_cutoff_hard to only take atoms within the hard cutoff radius.

cluster_cutoff_hard:

type: float, optional

argument path: simplify_jdata[gaussian]/cluster_cutoff_hard

The hard cutoff radius of clusters if use_clusters is set to true. Outside the hard cutoff radius, atoms will not be taken even if they are in a molecule where some atoms are within the cutoff radius.

cluster_minify:

type: bool, optional, default: False

argument path: simplify_jdata[gaussian]/cluster_minify

If enabled, when an atom within the soft cutoff radius connects a single bond with a non-hydrogen atom out of the soft cutoff radius, the outer atom will be replaced by a hydrogen atom. When the outer atom is a hydrogen atom, the outer atom will be kept. In this case, other atoms out of the soft cutoff radius will be removed.

fp_params:

type: dict

argument path: simplify_jdata[gaussian]/fp_params

Parameters for Gaussian calculation.

keywords:

type: str | list[str]

argument path: simplify_jdata[gaussian]/fp_params/keywords

Keywords for Gaussian input, e.g. force b3lyp/6-31g**. If a list, run multiple steps.

multiplicity:

type: str | int, optional, default: auto

argument path: simplify_jdata[gaussian]/fp_params/multiplicity

Spin multiplicity for Gaussian input. If auto, multiplicity will be detected automatically, with the following rules: when fragment_guesses=True, multiplicity will +1 for each radical, and +2 for each oxygen molecule; when fragment_guesses=False, multiplicity will be 1 or 2, but +2 for each oxygen molecule.

nproc:

type: int

argument path: simplify_jdata[gaussian]/fp_params/nproc

The number of processors for Gaussian input.

charge:

type: int, optional, default: 0

argument path: simplify_jdata[gaussian]/fp_params/charge

Molecule charge. Only used when charge is not provided by the system.

fragment_guesses:

type: bool, optional, default: False

argument path: simplify_jdata[gaussian]/fp_params/fragment_guesses

Initial guess generated from fragment guesses. If True, multiplicity should be auto.

basis_set:

type: str, optional

argument path: simplify_jdata[gaussian]/fp_params/basis_set

Custom basis set.

keywords_high_multiplicity:

type: str, optional

argument path: simplify_jdata[gaussian]/fp_params/keywords_high_multiplicity

Keywords for points with multiple raicals. multiplicity should be auto. If not set, fallback to normal keywords.

When fp_style is set to siesta:

use_clusters:

type: bool, optional

argument path: simplify_jdata[siesta]/use_clusters

If set to true, clusters will be taken instead of the whole system. This option does not work with DeePMD-kit 0.x.

cluster_cutoff:

type: float, optional

argument path: simplify_jdata[siesta]/cluster_cutoff

The cutoff radius of clusters if use_clusters is set to true.

fp_params:

type: dict

argument path: simplify_jdata[siesta]/fp_params

Parameters for siesta calculation.

ecut:

type: int

argument path: simplify_jdata[siesta]/fp_params/ecut

Define the plane wave cutoff for grid.

ediff:

type: float

argument path: simplify_jdata[siesta]/fp_params/ediff

Tolerance of Density Matrix.

kspacing:

type: float

argument path: simplify_jdata[siesta]/fp_params/kspacing

Sample factor in Brillouin zones.

mixingWeight:

type: float

argument path: simplify_jdata[siesta]/fp_params/mixingWeight

Proportion a of output Density Matrix to be used for the input Density Matrix of next SCF cycle (linear mixing).

NumberPulay:

type: int

argument path: simplify_jdata[siesta]/fp_params/NumberPulay

Controls the Pulay convergence accelerator.

fp_pp_path:

type: str

argument path: simplify_jdata[siesta]/fp_pp_path

Directory of psuedo-potential or numerical orbital files to be used for 02.fp exists.

fp_pp_files:

type: list[str]

argument path: simplify_jdata[siesta]/fp_pp_files

Psuedo-potential file to be used for 02.fp. Note that the order of elements should correspond to the order in type_map.

When fp_style is set to cp2k:

user_fp_params:

type: dict, optional, alias: fp_params

argument path: simplify_jdata[cp2k]/user_fp_params

Parameters for cp2k calculation. find detail in manual.cp2k.org. only the kind section must be set before use. we assume that you have basic knowledge for cp2k input.
external_input_path:
type: str, optional

argument path: simplify_jdata[cp2k]/external_input_path

Conflict with key:user_fp_params. enable the template input provided by user. some rules should be followed, read the following text in detail:
One must present a KEYWORD ABC in the section CELL so that the DP-GEN can replace the cell on-the-fly.
One need to add these lines under FORCE_EVAL section to print forces and stresses:
STRESS_TENSOR ANALYTICAL
  &PRINT
    &FORCES ON
    &END FORCES
    &STRESS_TENSOR ON
    &END STRESS_TENSOR
  &END PRINT
When fp_style is set to abacus:

fp_pp_path:

type: str

argument path: simplify_jdata[abacus]/fp_pp_path

Directory of psuedo-potential or numerical orbital files to be used for 02.fp exists.

fp_pp_files:

type: list[str]

argument path: simplify_jdata[abacus]/fp_pp_files

Psuedo-potential file to be used for 02.fp. Note that the order of elements should correspond to the order in type_map.

fp_orb_files:

type: list[str], optional

argument path: simplify_jdata[abacus]/fp_orb_files

numerical orbital file to be used for 02.fp when using LCAO basis. Note that the order of elements should correspond to the order in type_map.

fp_incar:

type: str, optional

argument path: simplify_jdata[abacus]/fp_incar

Input file for ABACUS. This is optinal but the priority is lower than user_fp_params, and you should not set user_fp_params if you want to use fp_incar.

fp_kpt_file:

type: str, optional

argument path: simplify_jdata[abacus]/fp_kpt_file

KPT file for ABACUS.If the “kspacing” or “gamma_only=1” is defined in INPUT or “k_points” is defined, fp_kpt_file will be ignored.

fp_dpks_descriptor:

type: str, optional

argument path: simplify_jdata[abacus]/fp_dpks_descriptor

DeePKS descriptor file name. The file should be in pseudopotential directory.

user_fp_params:

type: dict, optional

argument path: simplify_jdata[abacus]/user_fp_params

Set the key and value of INPUT.

k_points:

type: list[int], optional

argument path: simplify_jdata[abacus]/k_points

Monkhorst-Pack k-grids setting for generating KPT file of ABACUS, such as: [1,1,1,0,0,0]. NB: if “kspacing” or “gamma_only=1” is defined in INPUT, k_points will be ignored.

When fp_style is set to pwmat:

TODO: add doc

When fp_style is set to pwscf:

pwscf (Quantum Espresso).

fp_pp_path:

type: str

argument path: simplify_jdata[pwscf]/fp_pp_path

Directory of psuedo-potential file to be used for 02.fp exists.

fp_pp_files:

type: list[str]

argument path: simplify_jdata[pwscf]/fp_pp_files

Psuedo-potential file to be used for 02.fp. Note that the order of elements should correspond to the order in type_map.

fp_params:

type: dict, optional

argument path: simplify_jdata[pwscf]/fp_params

Parameters for pwscf calculation. It has lower priority than user_fp_params.

ecut:

type: float

argument path: simplify_jdata[pwscf]/fp_params/ecut

ecutwfc in pwscf.

ediff:

type: float

argument path: simplify_jdata[pwscf]/fp_params/ediff

conv_thr and ts_vdw_econv_thr in pwscf.

smearing:

type: str

argument path: simplify_jdata[pwscf]/fp_params/smearing

smearing in pwscf.

sigma:

type: float

argument path: simplify_jdata[pwscf]/fp_params/sigma

degauss in pwscf.

kspacing:

type: float

argument path: simplify_jdata[pwscf]/fp_params/kspacing

The spacing between kpoints. Helps to determin KPOINTS in pwscf.

user_fp_params:

type: dict, optional

argument path: simplify_jdata[pwscf]/user_fp_params

Parameters for pwscf calculation. Find details at https://www.quantum-espresso.org/Doc/INPUT_PW.html. When user_fp_params is set, the settings in fp_params will be ignored. If one wants to use user_fp_params, kspacing must be set in user_fp_params. kspacing is the spacing between kpoints, and helps to determin KPOINTS in pwscf.

When fp_style is set to custom:

Custom FP code. You need to provide the input and output file format and name. The command argument in the machine file should be the script to run custom FP codes. The extra forward and backward files can be defined in the machine file.

fp_params:

type: dict

argument path: simplify_jdata[custom]/fp_params

Parameters for FP calculation.

input_fmt:

type: str

argument path: simplify_jdata[custom]/fp_params/input_fmt

Input dpdata format of the custom FP code. Such format should only need the first argument as the file name.

input_fn:

type: str

argument path: simplify_jdata[custom]/fp_params/input_fn

Input file name of the custom FP code.

output_fmt:

type: str

argument path: simplify_jdata[custom]/fp_params/output_fmt

Output dpata format of the custom FP code. Such format should only need the first argument as the file name.

output_fn:

type: str

argument path: simplify_jdata[custom]/fp_params/output_fn

Output file name of the custom FP code.