Source code for stk.molecular.topology_graphs.topology_graph.optimizers.collapser



import mchammer as mch

from .optimizer import Optimizer
from .utilities import get_long_bond_ids, get_mch_bonds, get_subunits

[docs]class Collapser(Optimizer): """ Performs rigid-body collapse of molecules [1]_. Examples -------- *Structure Optimization* Using :class:`.Collapser` will lead to :class:`.ConstructedMolecule` structures without long bonds. .. testcode:: structure-optimization import stk bb1 = stk.BuildingBlock('NCCN', [stk.PrimaryAminoFactory()]) bb2 = stk.BuildingBlock('O=CCC=O', [stk.AldehydeFactory()]) polymer = stk.ConstructedMolecule( topology_graph=stk.polymer.Linear( building_blocks=(bb1, bb2), repeating_unit='AB', num_repeating_units=2, optimizer=stk.Collapser(), ), ) Optimisation with :mod:`stk` simply collects the final position matrix. The optimisation's trajectory can be output using the :mod:`MCHammer` implementation if required by the user [1]_. The open-source optimization code :mod:`MCHammer` specializes in the `collapsing` of molecules with long bonds like those constructed by :mod:`stk`. This code is entirely nonphysical and is, therefore, completely general to any chemistry. References ---------- .. [1] """
[docs] def __init__( self, step_size=0.1, distance_threshold=1.5, scale_steps=True, ): """ Initialize an instance of :class:`.Collapser`. Parameters ---------- step_size : :class:`float`, optional The relative size of the step to take during collapse in Angstrom. distance_threshold : :class:`float`, optional Distance between distinct building blocks to use as threshold for halting collapse in Angstrom. scale_steps : :class:`bool`, optional Whether to scale the step of each distinct building block by its relative distance from the molecules centroid. """ self._optimizer = mch.Collapser( step_size=step_size, distance_threshold=distance_threshold, scale_steps=scale_steps, )
[docs] def optimize(self, state): # Define MCHammer molecule to optimize. mch_mol = mch.Molecule( atoms=( mch.Atom( id=atom.get_id(), element_string=atom.__class__.__name__, ) for atom in state.get_atoms() ), bonds=get_mch_bonds(state), position_matrix=state.get_position_matrix(), ) # Run optimization. mch_mol, result = self._optimizer.get_result( mol=mch_mol, bond_pair_ids=tuple(get_long_bond_ids(state)), subunits=get_subunits(state), ) return state.with_position_matrix( position_matrix=mch_mol.get_position_matrix() )