Source code for

# Copyright 2016 Autodesk Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from .base import MinimizerBase
from . import toplevel, BFGS, GradientDescent, SequentialLeastSquares

from moldesign import utils
from moldesign import units as u

GDTHRESH = 1.5*u.eV/u.angstrom

def exports(o):
    return o
__all__ = []

[docs]class SmartMin(MinimizerBase): """ Uses gradient descent until forces fall below a threshold, then switches to BFGS (unconstrained) or SLSQP (constrained). Args: gd_threshold (u.Scalar[force]): Use gradient descent if there are any forces larger than this; use an approximate hessian method (BFGS or SLSQP) otherwise Note: Not really that smart. """ # TODO: use non-gradient methods if forces aren't available _strip_units = True @utils.args_from(MinimizerBase, inject_kwargs={'gd_threshold': GDTHRESH}) def __init__(self, *args, **kwargs): self.gd_threshold = kwargs.pop('gd_threshold', GDTHRESH) self.args = args self.kwargs = kwargs super(SmartMin, self).__init__(*args, **kwargs)
[docs] def run(self): # If forces are already low, go directly to the quadratic convergence methods and return forces = self.mol.calculate_forces() if abs(forces).max() <= self.gd_threshold: spmin = self._make_quadratic_method() self.traj = spmin.traj self.current_step = spmin.current_step return # Otherwise, remove large forces with gradient descent; exit if we pass the cycle limit descent_kwargs = self.kwargs.copy() descent_kwargs['force_tolerance'] = self.gd_threshold descender = GradientDescent(*self.args, **descent_kwargs) if descender.current_step >= self.nsteps: self.traj = descender.traj return # Finally, use a quadratic method to converge the optimization kwargs = dict(_restart_from=descender.current_step, _restart_energy=descender._initial_energy) kwargs['frame_interval'] = self.kwargs.get('frame_interval', descender.frame_interval) spmin = self._make_quadratic_method(kwargs) spmin.current_step = descender.current_step self.traj = descender.traj + spmin.traj self.current_step = spmin.current_step
def _make_quadratic_method(self, kwargs=None): if kwargs is None: kwargs = {} kw = self.kwargs.copy() kw.update(kwargs) if self.mol.constraints: spmin = SequentialLeastSquares(*self.args, **kw) else: spmin = BFGS(*self.args, **kw) return spmin
minimize = SmartMin._as_function('minimize') exports(minimize) toplevel(minimize)