def optimise_positions(self, fmax=0.01, use_precon=None, use_armijo=None):
""" Relax atoms positions with the fixed cell to a given force
threshold """
if use_precon is None:
use_precon = self.parameters.use_precon
if use_armijo is None:
use_armijo = self.parameters.use_armijo
if use_precon:
precon = Exp(A=3, use_pyamg=False)
else:
precon = None
if self.parameters.optimizer == 'BFGS':
relax = BFGS(self.atoms)
elif self.parameters.optimizer == 'FIRE':
relax = PreconFIRE(self.atoms, precon=precon)
elif self.parameters.optimizer == 'ase-FIRE':
relax = ASEFIRE(self.atoms)
elif self.parameters.optimizer == 'LBFGS':
relax = PreconLBFGS(self.atoms,
precon=precon,
use_armijo=use_armijo)
elif self.parameters.optimizer == 'ase-LBFGS':
relax = ASELBFGS(self.atoms)
else:
parprint("ERROR: unknown optimizer {}. "
"Reverting to BFGS".format(self.parameters.optimizer))
relax = BFGS(self.atoms)
name = self.atoms.get_chemical_formula()
relax.attach(
lambda: self.atoms.calc.write(name + '_relax.gpw', mode='all'))
relax.attach(lambda: write_atat_input(self.atoms, 'str_last.out'))
relax.run(fmax=fmax, steps=100)
if not relax.converged():
relax = ASELBFGS(self.atoms)
relax.run(fmax=fmax, steps=100)
if not relax.converged():
max_force = self.atoms.get_forces()
max_force = np.sqrt((max_force**2).sum(axis=1).max())
print('WARNING: optimisation not converged.' +
' Maximum force: %.4f' % max_force)
