Source code for ase.optimize.fire

import numpy as np

from ase.optimize.optimize import Optimizer


[docs]class FIRE(Optimizer): def __init__(self, atoms, restart=None, logfile='-', trajectory=None, dt=0.1, maxmove=0.2, dtmax=1.0, Nmin=5, finc=1.1, fdec=0.5, astart=0.1, fa=0.99, a=0.1, master=None, downhill_check=False, position_reset_callback=None, force_consistent=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. logfile: file object or str If *logfile* is a string, a file with that name will be opened. Use '-' for stdout. master: boolean Defaults to None, which causes only rank 0 to save files. If set to true, this rank will save files. downhill_check: boolean Downhill check directly compares potential energies of subsequent steps of the FIRE algorithm rather than relying on the current product v*f that is positive if the FIRE dynamics moves downhill. This can detect numerical issues where at large time steps the step is uphill in energy even though locally v*f is positive, i.e. the algorithm jumps over a valley because of a too large time step. position_reset_callback: function(atoms, r, e, e_last) Function that takes current *atoms* object, an array of position *r* that the optimizer will revert to, current energy *e* and energy of last step *e_last*. This is only called if e > e_last. force_consistent: boolean or None Use force-consistent energy calls (as opposed to the energy extrapolated to 0 K). By default (force_consistent=None) uses force-consistent energies if available in the calculator, but falls back to force_consistent=False if not. Only meaningful when downhill_check is True. """ Optimizer.__init__(self, atoms, restart, logfile, trajectory, master, force_consistent=force_consistent) self.dt = dt self.Nsteps = 0 self.maxmove = maxmove self.dtmax = dtmax self.Nmin = Nmin self.finc = finc self.fdec = fdec self.astart = astart self.fa = fa self.a = a self.downhill_check = downhill_check self.position_reset_callback = position_reset_callback def initialize(self): self.v = None 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)) if self.downhill_check: self.e_last = atoms.get_potential_energy( force_consistent=self.force_consistent) self.r_last = atoms.get_positions().copy() self.v_last = self.v.copy() else: is_uphill = False if self.downhill_check: e = atoms.get_potential_energy( force_consistent=self.force_consistent) # Check if the energy actually decreased if e > self.e_last: # If not, reset to old positions... if self.position_reset_callback is not None: self.position_reset_callback(atoms, self.r_last, e, self.e_last) atoms.set_positions(self.r_last) is_uphill = True self.e_last = atoms.get_potential_energy( force_consistent=self.force_consistent) self.r_last = atoms.get_positions().copy() self.v_last = self.v.copy() vf = np.vdot(f, self.v) if vf > 0.0 and not is_uphill: self.v = (1.0 - self.a) * self.v + self.a * f / np.sqrt( np.vdot(f, f)) * np.sqrt(np.vdot(self.v, self.v)) if self.Nsteps > self.Nmin: self.dt = min(self.dt * self.finc, self.dtmax) self.a *= self.fa self.Nsteps += 1 else: self.v[:] *= 0.0 self.a = self.astart self.dt *= self.fdec self.Nsteps = 0 self.v += self.dt * f dr = self.dt * self.v normdr = np.sqrt(np.vdot(dr, dr)) if normdr > self.maxmove: dr = self.maxmove * dr / normdr r = atoms.get_positions() atoms.set_positions(r + dr) self.dump((self.v, self.dt))