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 | str, 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”. Tips: at present the default value will not be applied automatically, so you need to set “mass_map” manually in param.json.

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: list | 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.

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: NoneType | float, optional

argument path: simplify_jdata/training_reuse_old_ratio

The probability proportion of old data during training. 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: 400000, 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: NoneType | float, optional, default: 0.0001

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 | NoneType | float, optional, default: 0.1

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 | NoneType | float, optional, default: 100

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: list | NoneType, 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.

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.

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, 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.

ratio_failed:

type: float, optional

argument path: simplify_jdata[vasp]/ratio_failed

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

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: 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: int | str, 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.

ratio_failed:

type: float, optional

argument path: simplify_jdata[gaussian]/ratio_failed

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