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README_usage.md

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Usage

pymatnext [ --random_seed / s <seed> ] [ --output_file_postfix / -p <postfix> ] [ --max_iter / -i <max iter> ] <params_file>

Do a nested sampling run based on the parameters in <params_file> in toml format.

Command line arguments

  • --random_seed / s <seed>: set a random seed (overriding parameter file)
  • --max_iter / -i <max_iter>: maximum NS iteration (overriding arameter file)
  • --output_file_postfix / -p <postfix>: a suffix to all output files that is added to the parameter file value

By setting different random seeds and output postfix strings, multiple independent runs can be started (for better sampling) without having to modify the parameter file.

Output

Nested sampling quantities

  • sampled quantities in <global.output_file_prefix><output_file_postfix>.NS_samples

    • JSON format header line, prefixed by #, describing NS run parameters and quantities in file, for analysis
    • one line every <global.sample_interval> NS iterations, with iteration number, NS quantity, and configuration-specific quantities specified in header extra dict item.

  • sampled configurations in <global.output_file_prefix><output_file_postfix>.traj.<filename_suffix>

    • One configuration in a type-specific format (extxyz for atomic configurations) every global.traj_interval NS iterations

  • snapshots

    • NS state in <global.output_file_prefix><output_file_postfix>.iter_<iter>.state.json>
    • NS configurations in <global.output_file_prefix><output_file_postfix>.iter_<iter>.configs.<filename_suffix>>

Example

Full featured example of a small system with variable cell, semi-grand-canonical run using LAMMPS internal propagators.

[global]

    output_filename_prefix = "EAM_LAMMPS_sGC"
    random_seed = 5

    max_iter = 50000

    stdout_report_interval = 60

    snapshot_interval = 50000

    [global.step_size_tune]

        interval = 1000

[ns]

    n_walkers = 280
    walk_length = 400
    configs_module = "pymatnext.ns_configs.ase_atoms"

    [ns.exit_conditions]

        module = "pymatnext.loop_exit.Z_of_T"

        [ns.exit_conditions.module_kwargs]
            T_to_ns_quant = "kB"
            T = 5000.0

[configs]

    composition = "AlCu"
    n_atoms = 16
    initial_rand_vol_per_atom = 800.0
    initial_rand_min_dist = 0.5

    [configs.calculator]

        type = "LAMMPS"

        [configs.calculator.args]

            cmds = ["pair_style eam/alloy", "pair_coeff * * <PATH_TO_POTENTIALS>/AlCu_Zhou04.eam.alloy Al Cu"]

            types.13 = 1
            types.29 = 2

    [configs.walk]

        gmc_traj_len = 8
        cell_traj_len = 4
        type_traj_len = 8

        gmc_proportion = 1.0
        cell_proportion = 1.5
        type_proportion = 1.0

        [configs.walk.cell]

            [configs.walk.cell.submove_probabilities]
                volume = 0.33
                shear = 0.33
                stretch = 0.33

        [configs.walk.type]
            sGC = true
            mu.13 = 0.1
            mu.29 = 0.2

Hopefully section and key names are self explanatory.

Additional notes on run parameters

  • all intervals are in NS iterations, except stdout_report_interval_s which is in seconds
  • configs.calculator.type can be "ASE" or "LAMMPS"
    • if "LAMMPS", args consists of cmds, with LAMMPS commands, and types dict (one key for each species)
    • if "ASE", args consists of module key with module that defines a calc symbol containing an ase.calculators.Calculator object
  • configs.walk.*_traj_len controls the number of steps in a walk block of that type
  • configs.walk.*_proportion controls the fraction of steps overall that are used for that type of move
  • if config.walk.type.sGC = true, a dict of mu values, one per species, is required.