dpgen simplify parameters

simplify_jdata:
type: dict
argument path: simplify_jdata

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

type_map:
type: list
argument path: simplify_jdata/type_map

Atom types.

mass_map:
type: list
argument path: simplify_jdata/mass_map

Standard atom weights.

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
argument path: simplify_jdata/init_data_sys

Directories of initial data. You may use either absolute or relative path here. Systems will be detected recursively in the directories.

sys_format:
type: str, optional, default: vasp/poscar
argument path: simplify_jdata/sys_format

Format of initial data.

init_batch_size:
type: 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.

sys_configs_prefix:
type: str, optional
argument path: simplify_jdata/sys_configs_prefix

Prefix of sys_configs.

sys_configs:
type: list
argument path: simplify_jdata/sys_configs

Containing directories of structures to be explored in iterations.Wildcard characters are supported here.

sys_batch_size:
type: list, 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.

labeled:
type: bool, optional, default: False
argument path: simplify_jdata/labeled

If true, the initial data is labeled.

pick_data:
type: str
argument path: simplify_jdata/pick_data

Path to the directory with the pick data with the deepmd/npy 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.

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, 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 here: (https://github.com/deepmodeling/deepmd-kit).

dp_compress:
type: bool, optional, default: False
argument path: simplify_jdata/dp_compress

Use dp compress to compress the model.

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.

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

Software for First Principles, if labeled is false. Options include “vasp”, “gaussian” up to now.

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
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
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
argument path: simplify_jdata[vasp]/cvasp

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

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. This option does not work with DeePMD-kit 0.x.

cluster_cutoff:
type: float, optional
argument path: simplify_jdata[gaussian]/cluster_cutoff

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

fp_params:
type: dict
argument path: simplify_jdata[gaussian]/fp_params

Parameters for Gaussian calculation.

keywords:
type: str
argument path: simplify_jdata[gaussian]/fp_params/keywords

Keywords for Gaussian input.

multiplicity:
type: int
argument path: simplify_jdata[gaussian]/fp_params/multiplicity

Spin multiplicity for Gaussian input. If set to auto, the spin multiplicity will be detected automatically. If set to frag, the “fragment=N” method will be used.

nproc:
type: int
argument path: simplify_jdata[gaussian]/fp_params/nproc

The number of processors for Gaussian input.