dpgen simplify parameters
Note
One can load, modify, and export the input file by using our effective web-based tool DP-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
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
, 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
, optionalargument path:simplify_jdata/init_data_prefix
Prefix of initial data directories.
- init_data_sys:
- type:
list
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
, 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
|list
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.
- 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.
- training_reuse_iter:
- type:
int
|NoneType
, optionalargument 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:
float
|NoneType
, optionalargument 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_batchargument 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: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
|float
|NoneType
, 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
|float
|NoneType
, 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
, optionalargument 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
, optionalargument 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
Before training, all of the training data will be merged into one HDF5 file.
- 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.
- fp_accurate_threshold:
- type:
float
, optionalargument 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
, optionalargument 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:
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
, optionalargument 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
, optionalargument path:simplify_jdata[vasp]/cvasp
If cvasp is true, DP-GEN will use Custodian to help control VASP calculation.
- ratio_failed:
- type:
float
, optionalargument 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
, optionalargument 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
, optionalargument 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
, optionalargument 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
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
, optionalargument path:simplify_jdata[gaussian]/fp_params/basis_set
Custom basis set.
- keywords_high_multiplicity:
- type:
str
, optionalargument 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
, optionalargument 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.