dpgen run param 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.

run_jdata:#

type: dict
argument path: run_jdata
param.json file
type_map:#
type: list[str]
argument path: run_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: run_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: run_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: run_jdata/init_data_prefix
Prefix of initial data directories.
init_data_sys:#
type: list[str]
argument path: run_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: run_jdata/sys_format
Format of sys_configs.
init_batch_size:#
type: str | list[typing.Union[int, str]], optional
argument path: run_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: run_jdata/sys_configs_prefix
Prefix of sys_configs.
sys_configs:#
type: list[list[str]]
argument path: run_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: run_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.
numb_models:#
type: int
argument path: run_jdata/numb_models
Number of models to be trained in 00.train. 4 is recommend.
fp_task_max:#
type: int
argument path: run_jdata/fp_task_max
Maximum number of structures to be calculated in each system in 02.fp of each iteration. If the number of candidate structures exceeds fp_task_max, fp_task_max structures will be randomly picked from the candidates and labeled.
fp_task_min:#
type: int
argument path: run_jdata/fp_task_min
Skip the training in the next iteration if the number of structures is no more than fp_task_min.
fp_accurate_threshold:#
type: float, optional
argument path: run_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: run_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.
fp_cluster_vacuum:#
type: float, optional
argument path: run_jdata/fp_cluster_vacuum
If the vacuum size is smaller than this value, this cluster will not be chosen for labeling.
detailed_report_make_fp:#
type: bool, optional, default: True
argument path: run_jdata/detailed_report_make_fp
If set to true, a detailed report will be generated for each iteration.
ratio_failed:#
type: float, optional
argument path: run_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 mlp_engine, different sub args are accepted.
mlp_engine:#
type: str (flag key), default: dp
argument path: run_jdata/mlp_engine
possible choices: dp
Machine learning potential engine. Currently, only DeePMD-kit (defualt) is supported.
dp: DeePMD-kit.
When mlp_engine is set to dp:
DeePMD-kit.
train_backend:#
type: str, optional, default: tensorflow
argument path: run_jdata[mlp_engine=dp]/train_backend
The backend of the training. Currently only support tensorflow and pytorch.
training_iter0_model_path:#
type: list[str], optional
argument path: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/dp_train_skip_neighbor_stat
Append –skip-neighbor-stat flag to dp train.
dp_compress:#
type: bool, optional, default: False
argument path: run_jdata[mlp_engine=dp]/dp_compress
Use dp compress to compress the model.
training_reuse_iter:#
type: int | NoneType, optional
argument path: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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[list[str]] | NoneType, optional
argument path: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/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: run_jdata[mlp_engine=dp]/training_finetune_model
At interation 0, finetune the model parameters from the given frozen models. Number of element should be equal to numb_models.
Depending on the value of model_devi_engine, different sub args are accepted.
model_devi_engine:#
type: str (flag key), default: lammps
argument path: run_jdata/model_devi_engine
possible choices: lammps, amber, calypso, gromacs
Engine for the model deviation task.
lammps: LAMMPS
amber: Amber DPRc engine. The command argument in the machine file should be path to sander.
calypso: TODO: add doc
gromacs: TODO: add doc
When model_devi_engine is set to lammps:
LAMMPS
model_devi_jobs:#
type: list
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs
Settings for exploration in 01.model_devi. Each dict in the list corresponds to one iteration. The index of model_devi_jobs exactly accord with index of iterations
This argument takes a list with each element containing the following:
template:#
type: dict, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/template
Give an input file template for the supported engine software adopted in 01.model_devi. Through user-defined template, any freedom (function) that is permitted by the engine software could be inherited (invoked) in the workflow.
lmp:#
type: str, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/template/lmp
The path to input.lammps template. Instructions can be found in LAMMPS documentation.
plm:#
type: str, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/template/plm
The path to input.plumed template. Instructions can be found in PLUMED documentation.
rev_mat:#
type: dict, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/rev_mat
revise matrix for revising variable(s) defined in the template into the specific values (iteration-resolved). Values will be broadcasted for all tasks within the iteration invoking this key.
lmp:#
type: dict, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/rev_mat/lmp
revise matrix for revising variable(s) defined in the lammps template into the specific values (iteration-resolved).
plm:#
type: dict, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/rev_mat/plm
revise matrix for revising variable(s) defined in the plumed template into specific values(iteration-resolved)
sys_rev_mat:#
type: dict, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/sys_rev_mat
system-resolved revise matrix for revising variable(s) defined in the template into specific values. Values should be individually assigned to each system adopted by this iteration, through a dictionary where first-level keys are values of sys_idx of this iteration.
sys_idx:#
type: list[int]
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/sys_idx
Systems to be selected as the initial structure of MD and be explored. The index corresponds exactly to the sys_configs.
temps:#
type: list[float], optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/temps
Temperature (K) in MD.
press:#
type: list[float], optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/press
Pressure (Bar) in MD. Required when ensemble is npt.
trj_freq:#
type: int
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/trj_freq
Frequecy of trajectory saved in MD.
nsteps:#
type: int, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/nsteps
Running steps of MD. It is not optional when not using a template.
nbeads:#
type: int, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/nbeads
Number of beads in PIMD. If not given, classical MD will be performed. Only supported for LAMMPS version >= 20230615.
ensemble:#
type: str, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/ensemble
Determining which ensemble used in MD, options include “npt” and “nvt”. It is not optional when not using a template.
neidelay:#
type: int, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/neidelay
delay building until this many steps since last build.
taut:#
type: float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/taut
Coupling time of thermostat (ps).
taup:#
type: float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/taup
Coupling time of barostat (ps).
model_devi_f_trust_lo:#
type: dict | float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/model_devi_f_trust_lo
Lower bound of forces for the selection. If dict, should be set for each index in sys_idx, respectively.
model_devi_f_trust_hi:#
type: dict | float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/model_devi_f_trust_hi
Upper bound of forces for the selection. If dict, should be set for each index in sys_idx, respectively.
model_devi_v_trust_lo:#
type: dict | float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/model_devi_v_trust_lo
Lower bound of virial for the selection. If dict, should be set for each index in sys_idx, respectively. Should be used with DeePMD-kit v2 or above.
model_devi_v_trust_hi:#
type: dict | float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_jobs/model_devi_v_trust_hi
Upper bound of virial for the selection. If dict, should be set for each index in sys_idx, respectively. Should be used with DeePMD-kit v2 or above.
model_devi_dt:#
type: float
argument path: run_jdata[model_devi_engine=lammps]/model_devi_dt
Timestep for MD. 0.002 is recommend.
model_devi_skip:#
type: int
argument path: run_jdata[model_devi_engine=lammps]/model_devi_skip
Number of structures skipped for fp in each MD.
model_devi_f_trust_lo:#
type: dict | list[float] | float
argument path: run_jdata[model_devi_engine=lammps]/model_devi_f_trust_lo
Lower bound of forces for the selection. If list or dict, should be set for each index in sys_configs, respectively.
model_devi_f_trust_hi:#
type: dict | list[float] | float
argument path: run_jdata[model_devi_engine=lammps]/model_devi_f_trust_hi
Upper bound of forces for the selection. If list or dict, should be set for each index in sys_configs, respectively.
model_devi_v_trust_lo:#
type: dict | list[float] | float, optional, default: 10000000000.0
argument path: run_jdata[model_devi_engine=lammps]/model_devi_v_trust_lo
Lower bound of virial for the selection. If list or dict, should be set for each index in sys_configs, respectively. Should be used with DeePMD-kit v2 or above.
model_devi_v_trust_hi:#
type: dict | list[float] | float, optional, default: 10000000000.0
argument path: run_jdata[model_devi_engine=lammps]/model_devi_v_trust_hi
Upper bound of virial for the selection. If list or dict, should be set for each index in sys_configs, respectively. Should be used with DeePMD-kit v2 or above.
model_devi_adapt_trust_lo:#
type: bool, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_adapt_trust_lo
Adaptively determines the lower trust levels of force and virial. This option should be used together with model_devi_numb_candi_f, model_devi_numb_candi_v and optionally with model_devi_perc_candi_f and model_devi_perc_candi_v. dpgen will make two sets:
From the frames with force model deviation lower than model_devi_f_trust_hi, select max(model_devi_numb_candi_f, model_devi_perc_candi_f*n_frames) frames with largest force model deviation.
From the frames with virial model deviation lower than model_devi_v_trust_hi, select max(model_devi_numb_candi_v, model_devi_perc_candi_v*n_frames) frames with largest virial model deviation.
The union of the two sets is made as candidate dataset.
model_devi_numb_candi_f:#
type: int, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_numb_candi_f
See model_devi_adapt_trust_lo.
model_devi_numb_candi_v:#
type: int, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_numb_candi_v
See model_devi_adapt_trust_lo.
model_devi_perc_candi_f:#
type: float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_perc_candi_f
See model_devi_adapt_trust_lo.
model_devi_perc_candi_v:#
type: float, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_perc_candi_v
See model_devi_adapt_trust_lo.
model_devi_f_avg_relative:#
type: bool, optional
argument path: run_jdata[model_devi_engine=lammps]/model_devi_f_avg_relative
Normalized the force model deviations by the RMS force magnitude along the trajectory. This key should not be used with use_relative.
model_devi_clean_traj:#
type: bool | int, optional, default: True
argument path: run_jdata[model_devi_engine=lammps]/model_devi_clean_traj
If type of model_devi_clean_traj is bool type then it denote whether to clean traj folders in MD since they are too large. If it is Int type, then the most recent n iterations of traj folders will be retained, others will be removed.
model_devi_merge_traj:#
type: bool, optional, default: False
argument path: run_jdata[model_devi_engine=lammps]/model_devi_merge_traj
If model_devi_merge_traj is set as True, only all.lammpstrj will be generated, instead of lots of small traj files.
model_devi_nopbc:#
type: bool, optional, default: False
argument path: run_jdata[model_devi_engine=lammps]/model_devi_nopbc
Assume open boundary condition in MD simulations.
model_devi_plumed:#
type: bool, optional, default: False
argument path: run_jdata[model_devi_engine=lammps]/model_devi_plumed
model_devi_plumed_path:#
type: bool, optional, default: False
argument path: run_jdata[model_devi_engine=lammps]/model_devi_plumed_path
shuffle_poscar:#
type: bool, optional, default: False
argument path: run_jdata[model_devi_engine=lammps]/shuffle_poscar
Shuffle atoms of each frame before running simulations. The purpose is to sample the element occupation of alloys.
use_relative:#
type: bool, optional, default: False
argument path: run_jdata[model_devi_engine=lammps]/use_relative
Calculate relative force model deviation.
epsilon:#
type: float, optional
argument path: run_jdata[model_devi_engine=lammps]/epsilon
The level parameter for computing the relative force model deviation.
use_relative_v:#
type: bool, optional, default: False
argument path: run_jdata[model_devi_engine=lammps]/use_relative_v
Calculate relative virial model deviation.
epsilon_v:#
type: float, optional
argument path: run_jdata[model_devi_engine=lammps]/epsilon_v
The level parameter for computing the relative virial model deviation.
lmp_d3:#
type: dict, optional
argument path: run_jdata[model_devi_engine=lammps]/lmp_d3
D3 dispersion configuration for LAMMPS. When present, all sub-parameters are required.
enable:#
type: bool
argument path: run_jdata[model_devi_engine=lammps]/lmp_d3/enable
Enable D3 dispersion correction. If false, D3 will be disabled even if other parameters are present.
damping_function:#
type: str
argument path: run_jdata[model_devi_engine=lammps]/lmp_d3/damping_function
Damping function for D3 dispersion. Options include ‘original’, ‘zerom’, ‘bj’, ‘bjm’.
functional:#
type: str
argument path: run_jdata[model_devi_engine=lammps]/lmp_d3/functional
Exchange-correlation functional for D3 dispersion. The functional should match that used to train MLPs.
cutoff:#
type: float
argument path: run_jdata[model_devi_engine=lammps]/lmp_d3/cutoff
Cutoff radius for D3 dispersion (in Angstrom).
cn_cutoff:#
type: float
argument path: run_jdata[model_devi_engine=lammps]/lmp_d3/cn_cutoff
Coordination number cutoff for D3 dispersion (in Angstrom).
lmp_neigh_modify_one:#
type: int, optional
argument path: run_jdata[model_devi_engine=lammps]/lmp_neigh_modify_one
Maximum number of neighbors of one atom for ‘neigh_modify one N’ command. Helps with D3 compatibility.
When model_devi_engine is set to amber:
Amber DPRc engine. The command argument in the machine file should be path to sander.
model_devi_jobs:#
type: list
argument path: run_jdata[model_devi_engine=amber]/model_devi_jobs
List of dicts. The list including the dict for information of each cycle.
This argument takes a list with each element containing the following:
sys_idx:#
type: list[int]
argument path: run_jdata[model_devi_engine=amber]/model_devi_jobs/sys_idx
List of ints. List of systems to run.
trj_freq:#
type: int
argument path: run_jdata[model_devi_engine=amber]/model_devi_jobs/trj_freq
Frequency to dump trajectory.
restart_from_iter:#
type: int, optional
argument path: run_jdata[model_devi_engine=amber]/model_devi_jobs/restart_from_iter
The iteration index to restart the simulation from. If not given, the simulation is restarted from sys_configs.
low_level:#
type: str
argument path: run_jdata[model_devi_engine=amber]/low_level
Low level method. The value will be filled into mdin file as @qm_theory@.
cutoff:#
type: float
argument path: run_jdata[model_devi_engine=amber]/cutoff
Cutoff radius for the DPRc model.
parm7_prefix:#
type: str, optional
argument path: run_jdata[model_devi_engine=amber]/parm7_prefix
The path prefix to AMBER PARM7 files.
parm7:#
type: list[str]
argument path: run_jdata[model_devi_engine=amber]/parm7
List of paths to AMBER PARM7 files. Each file maps to a system.
mdin_prefix:#
type: str, optional
argument path: run_jdata[model_devi_engine=amber]/mdin_prefix
The path prefix to AMBER mdin template files.
mdin:#
type: list[str]
argument path: run_jdata[model_devi_engine=amber]/mdin
List of paths to AMBER mdin template files. Each files maps to a system. In the template, the following keywords will be replaced by the actual value: @freq@: freq to dump trajectory; @nstlim@: total time step to run; @qm_region@: AMBER mask of the QM region; @qm_theory@: The low level QM theory, such as DFTB2; @qm_charge@: The total charge of the QM theory, such as -2; @rcut@: cutoff radius of the DPRc model; @GRAPH_FILE0@, @GRAPH_FILE1@, … : graph files.
qm_region:#
type: list[str]
argument path: run_jdata[model_devi_engine=amber]/qm_region
List of strings. AMBER mask of the QM region. Each mask maps to a system.
qm_charge:#
type: list[int]
argument path: run_jdata[model_devi_engine=amber]/qm_charge
List of ints. Charge of the QM region. Each charge maps to a system.
nsteps:#
type: list[int]
argument path: run_jdata[model_devi_engine=amber]/nsteps
List of ints. The number of steps to run. Each number maps to a system.
r:#
type: list[list[typing.Union[float, list[float]]]]
argument path: run_jdata[model_devi_engine=amber]/r
2D or 3D list of floats. Constrict values for the enhanced sampling. The first dimension maps to systems. The second dimension maps to confs in each system. The third dimension is the constrict value. It can be a single float for 1D or list of floats for nD.
disang_prefix:#
type: str, optional
argument path: run_jdata[model_devi_engine=amber]/disang_prefix
The path prefix to disang prefix.
disang:#
type: list[str]
argument path: run_jdata[model_devi_engine=amber]/disang
List of paths to AMBER disang files. Each file maps to a sytem. The keyword RVAL will be replaced by the constrict values, or RVAL1, RVAL2, … for an nD system.
model_devi_f_trust_lo:#
type: dict | list[float] | float
argument path: run_jdata[model_devi_engine=amber]/model_devi_f_trust_lo
Lower bound of forces for the selection. If dict, should be set for each index in sys_idx, respectively.
model_devi_f_trust_hi:#
type: dict | list[float] | float
argument path: run_jdata[model_devi_engine=amber]/model_devi_f_trust_hi
Upper bound of forces for the selection. If dict, should be set for each index in sys_idx, respectively.
When model_devi_engine is set to calypso:
TODO: add doc
When model_devi_engine is set to gromacs:
TODO: add doc
Depending on the value of fp_style, different sub args are accepted.
fp_style:#
type: str (flag key)
argument path: run_jdata/fp_style
possible choices: vasp, gaussian, siesta, cp2k, abacus, amber/diff, pwmat, pwscf, cpx, custom
Software for First Principles.
amber/diff: Amber/diff style for DPRc models. Note: this fp style only supports to be used with model_devi_engine amber, where some arguments are reused. The command argument in the machine file should be path to sander. One should also install dpamber and make it visible in the PATH.
pwmat: TODO: add doc
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.
When fp_style is set to vasp:
fp_pp_path:#
type: str
argument path: run_jdata[fp_style=vasp]/fp_pp_path
Directory of psuedo-potential file to be used for 02.fp exists.
fp_pp_files:#
type: list[str]
argument path: run_jdata[fp_style=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: run_jdata[fp_style=vasp]/fp_incar
Input file for VASP. INCAR must specify KSPACING and KGAMMA.
fp_aniso_kspacing:#
type: list[float], optional
argument path: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=vasp]/fp_skip_bad_box
Skip configurations with unreasonable simulation box geometries before first-principles calculations. This parameter accepts a semicolon-separated string of colon-separated key-value pairs defining geometric criteria. Example: ‘length_ratio:3;height_ratio:3;wrap_ratio:0.5;tilt_ratio:0.5’. Available criteria: (1) ‘length_ratio’: maximum ratio of cell edge lengths (max/min); (2) ‘height_ratio’: ratio of maximum cell edge length to minimum face-to-face distance; (3) ‘wrap_ratio’: maximum absolute ratio of off-diagonal to diagonal cell matrix elements, controlling triclinic wrapping; (4) ‘tilt_ratio’: maximum absolute tilt ratio for triclinic cells. Configurations exceeding any specified threshold are skipped.
When fp_style is set to gaussian:
use_clusters:#
type: bool, optional, default: False
argument path: run_jdata[fp_style=gaussian]/use_clusters
If set to true, clusters will be taken instead of the whole system.
cluster_cutoff:#
type: float, optional
argument path: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=gaussian]/fp_params
Parameters for Gaussian calculation.
keywords:#
type: str | list[str]
argument path: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=gaussian]/fp_params/nproc
The number of processors for Gaussian input.
charge:#
type: int, optional, default: 0
argument path: run_jdata[fp_style=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: run_jdata[fp_style=gaussian]/fp_params/fragment_guesses
Initial guess generated from fragment guesses. If True, multiplicity should be auto.
basis_set:#
type: str, optional
argument path: run_jdata[fp_style=gaussian]/fp_params/basis_set
Custom basis set.
keywords_high_multiplicity:#
type: str | list[str], optional
argument path: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=siesta]/cluster_cutoff
The cutoff radius of clusters if use_clusters is set to true.
fp_params:#
type: dict
argument path: run_jdata[fp_style=siesta]/fp_params
Parameters for siesta calculation.
ecut:#
type: int
argument path: run_jdata[fp_style=siesta]/fp_params/ecut
Define the plane wave cutoff for grid.
ediff:#
type: float
argument path: run_jdata[fp_style=siesta]/fp_params/ediff
Tolerance of Density Matrix.
kspacing:#
type: float
argument path: run_jdata[fp_style=siesta]/fp_params/kspacing
Sample factor in Brillouin zones.
mixingWeight:#
type: float
argument path: run_jdata[fp_style=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: run_jdata[fp_style=siesta]/fp_params/NumberPulay
Controls the Pulay convergence accelerator.
fp_pp_path:#
type: str
argument path: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=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: run_jdata[fp_style=abacus]/fp_dpks_descriptor
DeePKS descriptor file name. The file should be in pseudopotential directory.
user_fp_params:#
type: dict, optional
argument path: run_jdata[fp_style=abacus]/user_fp_params
Set the key and value of INPUT.
k_points:#
type: list[int], optional
argument path: run_jdata[fp_style=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 amber/diff:
Amber/diff style for DPRc models. Note: this fp style only supports to be used with model_devi_engine amber, where some arguments are reused. The command argument in the machine file should be path to sander. One should also install dpamber and make it visible in the PATH.
high_level:#
type: str
argument path: run_jdata[fp_style=amber/diff]/high_level
High level method. The value will be filled into mdin template as @qm_theory@.
fp_params:#
type: dict
argument path: run_jdata[fp_style=amber/diff]/fp_params
Parameters for FP calculation.
high_level_mdin:#
type: str
argument path: run_jdata[fp_style=amber/diff]/fp_params/high_level_mdin
Path to high-level AMBER mdin template file. %qm_theory%, %qm_region%, and %qm_charge% will be replaced.
low_level_mdin:#
type: str
argument path: run_jdata[fp_style=amber/diff]/fp_params/low_level_mdin
Path to low-level AMBER mdin template file. %qm_theory%, %qm_region%, and %qm_charge% will be replaced.
When fp_style is set to pwmat:
TODO: add doc
When fp_style is set to pwscf:
fp_pp_path:#
type: str
argument path: run_jdata[fp_style=pwscf]/fp_pp_path
Directory of psuedo-potential file to be used for 02.fp exists.
fp_pp_files:#
type: list[str]
argument path: run_jdata[fp_style=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: run_jdata[fp_style=pwscf]/fp_params
Parameters for pwscf calculation. It has lower priority than user_fp_params.
ecut:#
type: float
argument path: run_jdata[fp_style=pwscf]/fp_params/ecut
ecutwfc in pwscf.
ediff:#
type: float
argument path: run_jdata[fp_style=pwscf]/fp_params/ediff
conv_thr and ts_vdw_econv_thr in pwscf.
smearing:#
type: str
argument path: run_jdata[fp_style=pwscf]/fp_params/smearing
smearing in pwscf.
sigma:#
type: float
argument path: run_jdata[fp_style=pwscf]/fp_params/sigma
degauss in pwscf.
kspacing:#
type: float
argument path: run_jdata[fp_style=pwscf]/fp_params/kspacing
The spacing between kpoints. Helps to determin KPOINTS in pwscf.
user_fp_params:#
type: dict, optional
argument path: run_jdata[fp_style=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 cpx:
fp_params:#
type: dict
argument path: run_jdata[fp_style=cpx]/fp_params
Parameters for FP calculation.
input_fn:#
type: str
argument path: run_jdata[fp_style=cpx]/fp_params/input_fn
Input file name for cp.x without file extension (.in). Essentially must be the same as the prefix in the template.
template_fn:#
type: str
argument path: run_jdata[fp_style=cpx]/fp_params/template_fn
Template file name. Is used for completing the input file. Should contain all necessary FP configurations besides ATOMIC_POSITIONS and CELL_PARAMETERS
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: run_jdata[fp_style=custom]/fp_params
Parameters for FP calculation.
input_fmt:#
type: str
argument path: run_jdata[fp_style=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: run_jdata[fp_style=custom]/fp_params/input_fn
Input file name of the custom FP code.
output_fmt:#
type: str
argument path: run_jdata[fp_style=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: run_jdata[fp_style=custom]/fp_params/output_fn
Output file name of the custom FP code.