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
, optionalargument 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
, optionalargument 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
, optionalargument 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
, optionalargument 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
, optionalargument 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
, optionalargument 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
, optionalargument path:simplify_jdata/fp_task_max
Maximum of structures to be calculated in 02.fp of each iteration.
- fp_task_min:
- type:
int
, optionalargument 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:
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
, optionalargument 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.