machine_learning_fit

machine_learning_fit#

autoplex.fitting.common.jobs.machine_learning_fit(database_dir, species_list, run_fits_on_different_cluster=False, path_to_hyperparameters=None, isolated_atom_energies=None, num_processes_fit=32, auto_delta=True, glue_xml=False, glue_file_path='glue.xml', mlip_type=None, ref_energy_name='REF_energy', ref_force_name='REF_forces', ref_virial_name='REF_virial', use_defaults=True, device='cuda', database_dict=None, hyperpara_opt=False, **fit_kwargs)[source]#

Job for fitting potential(s).

Parameters:

  • database_dir (Str | Path) – Path to the directory containing the database.

  • species_list (list) – List of element names (strings) involved in the training dataset

  • run_fit_on_different_cluster (bool) – Whether to run fitting on different clusters.

  • path_to_hyperparameters (str or Path.) – Path to JSON file containing the MLIP hyperparameters.

  • isolated_atom_energies (dict) – Dictionary of isolated atoms energies.

  • num_processes_fit (int) – Number of processes for fitting.

  • auto_delta (bool) – Automatically determine delta for 2b, 3b and soap terms.

  • glue_xml (bool) – Use the glue.xml core potential instead of fitting 2b terms.

  • glue_file_path (str) – Name of the glue.xml file path.

  • mlip_type (str) – Choose one specific MLIP type to be fitted: ‘GAP’ | ‘J-ACE’ | ‘NEQUIP’ | ‘M3GNET’ | ‘MACE’

  • ref_energy_name (str) – Reference energy name.

  • ref_force_name (str) – Reference force name.

  • ref_virial_name (str) – Reference virial name.

  • use_defaults (bool) – If True, use default fitting parameters

  • device (str) – Device to be used for model fitting, either “cpu” or “cuda”.

  • database_dict (dict) – Dict including all training and test databases.

  • hyperpara_opt (bool) – Perform hyperparameter optimization using XPOT (XPOT: https://pubs.aip.org/aip/jcp/article/159/2/024803/2901815)

  • fit_kwargs (dict) – Additional keyword arguments for MLIP fitting.

  • run_fits_on_different_cluster (bool)