root / ase / optimize / fire.py @ 1
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import numpy as np |
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from ase.optimize.optimize import Optimizer |
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class FIRE(Optimizer): |
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def __init__(self, atoms, restart=None, logfile='-', trajectory=None, |
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dt=0.1, maxmove=0.2, dtmax=1.0, Nmin=5, finc=1.1, fdec=0.5, |
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astart=0.1, fa=0.99, a=0.1): |
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Optimizer.__init__(self, atoms, restart, logfile, trajectory)
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self.dt = dt
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self.Nsteps = 0 |
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self.maxmove = maxmove
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self.dtmax = dtmax
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self.Nmin = Nmin
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self.finc = finc
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self.fdec = fdec
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self.astart = astart
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self.fa = fa
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self.a = a
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def initialize(self): |
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self.v = None |
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def read(self): |
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self.v, self.dt = self.load() |
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def step(self,f): |
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atoms = self.atoms
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if self.v is None: |
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self.v = np.zeros((len(atoms), 3)) |
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else:
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vf = np.vdot(f, self.v)
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if vf > 0.0: |
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self.v = (1.0 - self.a) * self.v + self.a * f / np.sqrt( |
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np.vdot(f, f)) * np.sqrt(np.vdot(self.v, self.v)) |
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if self.Nsteps > self.Nmin: |
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self.dt = min(self.dt * self.finc, self.dtmax) |
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self.a *= self.fa |
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self.Nsteps += 1 |
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else:
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self.v[:] *= 0.0 |
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self.a = self.astart |
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self.dt *= self.fdec |
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self.Nsteps = 0 |
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# if vf < 0.0:
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# self.v[:] = 0.0
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# self.a = self.astart
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# self.dt *= self.fdec
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# self.Nsteps = 0
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# else:
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# self.v = (1.0 - self.a) * self.v + self.a * f * np.sqrt(
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# np.vdot(f, f) / np.vdot(self.v, self.v))
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# if self.Nsteps > self.Nmin:
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# dt = min(dt * self.finc, dtmax)
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# self.a *= self.fa
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# self.Nsteps += 1
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self.v += self.dt * f |
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dr = self.dt * self.v |
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normdr = np.sqrt(np.vdot(dr, dr)) |
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if normdr > self.maxmove: |
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dr = self.maxmove * dr / normdr
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r = atoms.get_positions() |
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atoms.set_positions(r + dr) |
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self.dump((self.v, self.dt)) |