Source code for ase.optimize.mdmin

import numpy as np

from ase.optimize.optimize import Optimizer


[docs]class MDMin(Optimizer): def __init__(self, atoms, restart=None, logfile='-', trajectory=None, dt=None, master=None): """Parameters: atoms: Atoms object The Atoms object to relax. restart: string Pickle file used to store hessian matrix. If set, file with such a name will be searched and hessian matrix stored will be used, if the file exists. trajectory: string Pickle file used to store trajectory of atomic movement. maxstep: float Used to set the maximum distance an atom can move per iteration (default value is 0.2 Angstroms). logfile: string Text file used to write summary information. master: boolean Defaults to None, which causes only rank 0 to save files. If set to true, this rank will save files. """ Optimizer.__init__(self, atoms, restart, logfile, trajectory, master) if dt is not None: self.dt = dt def initialize(self): self.v = None self.dt = 0.2 def read(self): self.v, self.dt = self.load() def step(self, f=None): atoms = self.atoms if f is None: f = atoms.get_forces() if self.v is None: self.v = np.zeros((len(atoms), 3)) else: self.v += 0.5 * self.dt * f # Correct velocities: vf = np.vdot(self.v, f) if vf < 0.0: self.v[:] = 0.0 else: self.v[:] = f * vf / np.vdot(f, f) self.v += 0.5 * self.dt * f r = atoms.get_positions() atoms.set_positions(r + self.dt * self.v) self.dump((self.v, self.dt))