def optimize(self, quenchRoutine=None,
**kwargs):
"""
Optimize the band
Notes
-----
the potential for the NEB optimization is not Hamiltonian. This
means that there is no meaningful energy associated with the
potential. Therefore, during the optimization, we can do gradient
following, but we can't rely on the energy for, e.g. determining
step size, which is the default behavior for many optimizers. This
can be worked around by choosing a small step size and a large
maxErise, or by using an optimizer that uses only gradients.
scipy.lbfgs_b seems to work with NEB pretty well, but lbfgs_py and
mylbfgs tend to fail. If you must use one of those try, e.g.
maxErise = 1., maxstep=0.01, tol=1e-2
:quenchRoutine: quench algorithm to use for optimization.
:quenchParams: parameters for the quench """
if quenchRoutine is None:
if self.quenchRoutine is None:
quenchRoutine = mylbfgs
else:
quenchRoutine = self.quenchRoutine
#combine default and passed params. passed params will overwrite default
quenchParams = dict([("nsteps", 300)] +
self.quenchParams.items() +
kwargs.items())
if quenchParams.has_key("iprint"):
self.iprint = quenchParams["iprint"]
if not quenchParams.has_key("logger"):
quenchParams["logger"] = logging.getLogger("pygmin.connect.neb.quench")
if self.use_minimizer_callback:
quenchParams["events"]=[self._step]
self.step = 0
qres = quenchRoutine(
self.active.reshape(self.active.size), self,
**quenchParams)
# if isinstance(qres, tuple): # for compatability with old and new quenchers
# qres = qres[4]
self.active[:,:] = qres.coords.reshape(self.active.shape)
if self.copy_potential:
for i in xrange(0,self.nimages):
pot = self.potential_list[i]
self.energies[i] = pot.getEnergy(self.coords[i,:])
else:
for i in xrange(0,self.nimages):
self.energies[i] = self.potential.getEnergy(self.coords[i,:])
res = Result()
res.path = self.coords
res.nsteps = qres.nsteps
res.energy = self.energies
res.rms = qres.rms
res.success = False
if qres.rms < quenchParams["tol"]:
res.success = True
return res
def optimize(self, quenchRoutine=None, **kwargs):
"""
Optimize the band
Notes
-----
the potential for the NEB optimization is not Hamiltonian. This
means that there is no meaningful energy associated with the
potential. Therefore, during the optimization, we can do gradient
following, but we can't rely on the energy for, e.g. determining
step size, which is the default behavior for many optimizers. This
can be worked around by choosing a small step size and a large
maxErise, or by using an optimizer that uses only gradients.
scipy.lbfgs_b seems to work with NEB pretty well, but lbfgs_py and
mylbfgs tend to fail. If you must use one of those try, e.g.
maxErise = 1., maxstep=0.01, tol=1e-2
:quenchRoutine: quench algorithm to use for optimization.
:quenchParams: parameters for the quench """
if quenchRoutine is None:
if self.quenchRoutine is None:
quenchRoutine = mylbfgs
else:
quenchRoutine = self.quenchRoutine
#combine default and passed params. passed params will overwrite default
quenchParams = dict([("nsteps", 300)] + self.quenchParams.items() +
kwargs.items())
if quenchParams.has_key("iprint"):
self.iprint = quenchParams["iprint"]
if not quenchParams.has_key("logger"):
quenchParams["logger"] = logging.getLogger(
"pygmin.connect.neb.quench")
if self.use_minimizer_callback:
quenchParams["events"] = [self._step]
self.step = 0
qres = quenchRoutine(self.active.reshape(self.active.size), self,
**quenchParams)
# if isinstance(qres, tuple): # for compatability with old and new quenchers
# qres = qres[4]
self.active[:, :] = qres.coords.reshape(self.active.shape)
if self.copy_potential:
for i in xrange(0, self.nimages):
pot = self.potential_list[i]
self.energies[i] = pot.getEnergy(self.coords[i, :])
else:
for i in xrange(0, self.nimages):
self.energies[i] = self.potential.getEnergy(self.coords[i, :])
res = Result()
res.path = self.coords
res.nsteps = qres.nsteps
res.energy = self.energies
res.rms = qres.rms
res.success = False
if qres.rms < quenchParams["tol"]:
res.success = True
return res
