# 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 ```json "type_map": [ "H", "C" ], "mass_map": [ 1, 12 ], ``` The basics related keys specify the basic information about the system. {dargs:argument}`type_map ` gives the atom types, i.e. "H" and "C". {dargs:argument}`mass_map ` gives the standard atom weights, i.e. "1" and "12". ## data The data related keys in param.json are given as follows ```json "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. {dargs:argument}`init_data_prefix ` and {dargs:argument}`init_data_sys ` specify the location of the init data. {dargs:argument}`sys_configs_prefix ` and {dargs:argument}`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 ```json "numb_models": 4, "default_training_param": { }, ``` The training related keys specify the details of training tasks. {dargs:argument}`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 ```json "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. {dargs:argument}`model_devi_dt ` specifies timestep for MD simulation. {dargs:argument}`model_devi_skip ` specifies the number of structures skipped for saving in each MD. {dargs:argument}`model_devi_f_trust_lo ` and {dargs:argument}`model_devi_f_trust_hi ` specify the lower and upper bound of model_devi of forces for the selection. {dargs:argument}`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 {dargs:argument}`model_devi_jobs `, {dargs:argument}`sys_idx ` specifies the group of structures used for model_devi calculations, {dargs:argument}`temps ` specifies the temperature (K) in MD, {dargs:argument}`press ` specifies the pressure (Bar) in MD, {dargs:argument}`trj_freq ` specifies the frequency of trajectory saved in MD, {dargs:argument}`nsteps ` specifies the running steps of MD, {dargs:argument}`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 {dargs:argument}`model_devi_jobs `. MD simulations are run for 300 and 3000 time steps with the first and second groups of structures in {dargs:argument}`sys_configs ` in 00 and 01 iterations. We choose to save all structures generated in MD simulations and have set {dargs:argument}`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 {dargs:argument}`model_devi_clean_traj ` to be an integer. ## labeling The labeling related keys in param.json are given as follows ```json "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. {dargs:argument}`fp_style ` specifies software for First Principles. {dargs:argument}`fp_task_max ` and {dargs:argument}`fp_task_min ` specify the minimum and maximum of structures to be calculated in `02.fp` of each iteration. {dargs:argument}`fp_pp_path ` and {dargs:argument}`fp_pp_files ` specify the location of the psuedo-potential file to be used for 02.fp. {dargs:argument}`run_jdata[fp_style=vasp]/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 {dargs:argument}`type_map `. All the keys of the DP-GEN are explained in detail in the section Parameters.