def __init__(self, dof, state=None, hooks=None, counter0=0):
self.minimizer = Minimizer(
dof.x0,
self.fun,
ConjugateGradient(),
NewtonLineSearch(),
None,
None,
anagrad=True,
verbose=False,
)
BaseOptimizer.__init__(self, dof, state, hooks, counter0)
def guess_geometry(graph, unit_cell=None, verbose=False):
"""Construct a molecular geometry based on a molecular graph.
This routine does not require initial coordinates and will give a very
rough picture of the initial geometry. Do not expect all details to be
in perfect condition. A subsequent optimization with a more accurate
level of theory is at least advisable.
Argument:
| ``graph`` -- The molecular graph of the system, see
:class:molmod.molecular_graphs.MolecularGraph
Optional argument:
| ``unit_cell`` -- periodic boundry conditions, see
:class:`molmod.unit_cells.UnitCell`
| ``verbose`` -- Show optimizer progress when True
"""
N = len(graph.numbers)
from molmod.minimizer import Minimizer, ConjugateGradient, \
NewtonLineSearch, ConvergenceCondition, StopLossCondition
search_direction = ConjugateGradient()
line_search = NewtonLineSearch()
convergence = ConvergenceCondition(grad_rms=1e-6, step_rms=1e-6)
stop_loss = StopLossCondition(max_iter=500, fun_margin=0.1)
ff = ToyFF(graph, unit_cell)
x_init = np.random.normal(0, 1, N * 3)
# level 1 geometry optimization: graph based
ff.dm_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# level 2 geometry optimization: graph based + pauli repulsion
ff.dm_quad = 1.0
ff.dm_reci = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# Add a little noise to avoid saddle points
x_init += np.random.uniform(-0.01, 0.01, len(x_init))
# level 3 geometry optimization: bond lengths + pauli
ff.dm_quad = 0.0
ff.dm_reci = 0.2
ff.bond_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# level 4 geometry optimization: bond lengths + bending angles + pauli
ff.bond_quad = 0.0
ff.bond_hyper = 1.0
ff.span_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
x_opt = x_init
mol = Molecule(graph.numbers, x_opt.reshape((N, 3)))
return mol
def tune_geometry(graph, mol, unit_cell=None, verbose=False):
"""Fine tune a molecular geometry, starting from a (very) poor guess of
the initial geometry.
Do not expect all details to be in perfect condition. A subsequent
optimization with a more accurate level of theory is at least advisable.
Arguments:
| ``graph`` -- The molecular graph of the system, see
:class:molmod.molecular_graphs.MolecularGraph
| ``mol`` -- A :class:molmod.molecules.Molecule class with the initial
guess of the coordinates
Optional argument:
| ``unit_cell`` -- periodic boundry conditions, see
:class:`molmod.unit_cells.UnitCell`
| ``verbose`` -- Show optimizer progress when True
"""
N = len(graph.numbers)
from molmod.minimizer import Minimizer, ConjugateGradient, \
NewtonLineSearch, ConvergenceCondition, StopLossCondition
search_direction = ConjugateGradient()
line_search = NewtonLineSearch()
convergence = ConvergenceCondition(grad_rms=1e-6, step_rms=1e-6)
stop_loss = StopLossCondition(max_iter=500, fun_margin=1.0)
ff = ToyFF(graph, unit_cell)
x_init = mol.coordinates.ravel()
# level 3 geometry optimization: bond lengths + pauli
ff.dm_reci = 0.2
ff.bond_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# level 4 geometry optimization: bond lengths + bending angles + pauli
ff.bond_quad = 0.0
ff.bond_hyper = 1.0
ff.span_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
x_opt = x_init
mol = Molecule(graph.numbers, x_opt.reshape((N, 3)))
return mol