MD

mlptrain.sampling.md.run_mlp_md(configuration: mlptrain.Configuration, mlp: MLPotential, temp: float, dt: float, interval: int, pressure: float | None = None, compress: float | None = None, init_temp: float | None = None, fbond_energy: dict | None = None, bbond_energy: dict | None = None, bias: 'mlptrain.Bias' | 'mlptrain.PlumedBias' | None = None, restart_files: List[str] | None = None, copied_substrings: Sequence[str] | None = None, kept_substrings: Sequence[str] | None = None, **kwargs) → mlptrain.Trajectory

Run molecular dynamics on a system using a MLP to predict energies and forces and ASE to drive dynamics. The function is executed in a temporary directory. Note that NPT simulations are currently only implemented in production runs and not in active learning.

directory. Note that NPT simulations are currently only implemented in production runs and not in active learning.

Parameters:

  • mlp – Machine learnt potential

  • temp – Temperature in K to initialise velocities and to run NVT MD, if temp=0 then will run NVE

  • init_temp – (float | None) Initial temperature to initialise momenta with. If None then will be set to temp

  • dt – (float) Time-step in fs

  • interval – (int) Interval between saving the geometry

  • pressure – pressure in bar to run Berendsen NPT MD, temperature and pressure must also be specified in order to run NPT dynamics.

  • compress – compressibility in bar^-1 to run Berendsen NPT MD, temperature and pressure must also be specified in order to run NPT dynamics.

  • bbond_energy – (dict | None) Additional energy to add to a breaking bond. e.g. bbond_energy={(0, 1), 0.1} Adds 0.1 eV to the ‘bond’ between atoms 0 and 1 as velocities shared between the atoms in the breaking bond direction

  • fbond_energy – (dict | None) As bbond_energy but in the direction to form a bond

  • bias – (mlptrain.Bias | mlptrain.PlumedBias) mlp-train constrain to use in the dynamics

  • restart_files – List of files which are needed for restarting the simulation

  • kept_substrings – List of substrings with which files are copied back from the temporary directory e.g. ‘.json’, ‘trajectory_1.traj’

  • copied_substrings – List of substrings with which files are copied to the temporary directory. Files required for MLPs are added to the list automatically

Keyword Arguments:

  • constraints – (List) List of ASE constraints to use in the dynamics e.g. [ase.constraints.Hookean(a1, a2, k, rt)]

  • write_plumed_setup – (bool) If True saves the PLUMED input file as plumed_setup.dat

Return type:

(mlptrain.Trajectory)