Example-of-param.json
We have provided different examples of param.json in dpgen/examples/run/. In this section, we give a description of the param.json, taking dpgen/examples/run/dp2.x-lammps-vasp/param_CH4_deepmd-kit-2.0.1.json as an example. This is a param.json for a gas-phase methane molecule. Here, DeePMD-kit (v2.x), LAMMPS and VASP codes are used for training, exploration and labeling respectively.
basics
The basics related keys in param.json are given as follows
"type_map": [
"H",
"C"
],
"mass_map": [
1,
12
],
The basics related keys specify the basic information about the system. “type_map” gives the atom types, i.e. “H” and “C”. “mass_map” gives the standard atom weights, i.e. “1” and “12”.
data
The data related keys in param.json are given as follows
"init_data_prefix": "....../init/",
"init_data_sys": [
"CH4.POSCAR.01x01x01/02.md/sys-0004-0001/deepmd"
],
"sys_configs_prefix": "....../init/",
"sys_configs": [
[
"CH4.POSCAR.01x01x01/01.scale_pert/sys-0004-0001/scale*/00000*/POSCAR"
],
[
"CH4.POSCAR.01x01x01/01.scale_pert/sys-0004-0001/scale*/00001*/POSCAR"
]
],
The data related keys specify the init data for training initial DP models and structures used for model_devi calculations. “init_data_prefix” and “init_data_sys” specify the location of the init data. “sys_configs_prefix” and “sys_configs” specify the location of the structures.
Here, the init data is provided at “…… /init/CH4.POSCAR.01x01x01/02.md/sys-0004-0001/deepmd”. These structures are divided into two groups and provided at “……/init/CH4.POSCAR.01x01x01/01.scale_pert/sys-0004-0001/scale*/00000*/POSCAR” and “……/init/CH4.POSCAR.01x01x01/01.scale_pert/sys-0004-0001/scale*/00001*/POSCAR”.
training
The training related keys in param.json are given as follows
"numb_models": 4,
"default_training_param": {
},
The training related keys specify the details of training tasks. “numb_models” specifies the number of models to be trained. “default_training_param” specifies the training parameters for deepmd-kit
.
Here, 4 DP models will be trained in 00.train
. A detailed explanation of training parameters can be found in DeePMD-kit’s documentation (https://docs.deepmodeling.com/projects/deepmd/en/master/).
exploration
The exploration related keys in param.json are given as follows
"model_devi_dt": 0.002,
"model_devi_skip": 0,
"model_devi_f_trust_lo": 0.05,
"model_devi_f_trust_hi": 0.15,
"model_devi_clean_traj": true,
"model_devi_jobs": [
{
"sys_idx": [
0
],
"temps": [
100
],
"press": [
1.0
],
"trj_freq": 10,
"nsteps": 300,
"ensemble": "nvt",
"_idx": "00"
},
{
"sys_idx": [
1
],
"temps": [
100
],
"press": [
1.0
],
"trj_freq": 10,
"nsteps": 3000,
"ensemble": "nvt",
"_idx": "01"
}
],
The exploration related keys specify the details of exploration tasks. “model_devi_dt” specifies timestep for MD simulation. “model_devi_skip” specifies the number of structures skipped for saving in each MD. “model_devi_f_trust_lo” and “model_devi_f_trust_hi” specify the lower and upper bound of model_devi of forces for the selection. “model_devi_clean_traj” specifies whether to clean traj folders in MD. If type of model_devi_clean_traj is boolean type then it denote whether to clean traj folders in MD since they are too large.In “model_devi_jobs”, “sys_idx” specifies the group of structures used for model_devi calculations, “temps” specifies the temperature (K) in MD, “press” specifies the pressure (Bar) in MD, “trj_freq” specifies the frequency of trajectory saved in MD, “nsteps” specifies the running steps of MD, “ensemble” specifies the ensemble used in MD, and “_idx” specifies the index of iteration.
Here, MD simulations are performed at the temperature of 100 K and the pressure of 1.0 Bar with an integrator time of 2 fs under the nvt ensemble. Two iterations are set in “model_devi_jobs”. MD simulations are run for 300 and 3000 time steps with the first and second groups of structures in “sys_configs” in 00 and 01 iterations. We choose to save all structures generated in MD simulations and have set "trj_freq"
as 10, so 30 and 300 structures are saved in 00 and 01 iterations. If the “max_devi_f” of saved structure falls between 0.05 and 0.15, DP-GEN will treat the structure as a candidate. We choose to clean traj folders in MD since they are too large. If you want to save the most recent n iterations of traj folders, you can set “model_devi_clean_traj” to be an integer.
labeling
The labeling related keys in param.json are given as follows
"fp_style": "vasp",
"shuffle_poscar": false,
"fp_task_max": 20,
"fp_task_min": 1,
"fp_pp_path": "....../methane/",
"fp_pp_files": [
"POTCAR"
],
"fp_incar": "....../INCAR_methane"
The labeling related keys specify the details of labeling tasks. “fp_style” specifies software for First Principles. “fp_task_max” and “fp_task_min” specify the minimum and maximum of structures to be calculated in 02.fp
of each iteration. “fp_pp_path” and “fp_pp_files” specify the location of the psuedo-potential file to be used for 02.fp. “fp_incar” specifies input file for VASP. INCAR must specify KSPACING and KGAMMA.
Here, a minimum of 1 and a maximum of 20 structures will be labeled using the VASP code with the INCAR provided at “……/INCAR_methane” and POTCAR provided at “……/methane/POTCAR” in each iteration. Note that the order of elements in POTCAR should correspond to the order in type_map
.
All the keys of the DP-GEN are explained in detail in the section Parameters.