#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Adsorbate procreation operators that adds an adsorbate to the surface of
a particle or given structure."""
from ..settings import (adsorbate_elements,
site_heights)
from ..utilities import (custom_warning,
is_list_or_tuple,
atoms_too_close_after_addition)
from ..adsorption_sites import (ClusterAdsorptionSites,
SlabAdsorptionSites)
from ..adsorbate_coverage import (ClusterAdsorbateCoverage,
SlabAdsorbateCoverage)
from ..build.action import (add_adsorbate_to_site,
remove_adsorbate_from_site,
remove_adsorbates_from_sites)
from ase.ga.offspring_creator import OffspringCreator
from ase.formula import Formula
from ase import Atoms
from operator import attrgetter
from itertools import chain
from copy import deepcopy
import numpy as np
import warnings
import random
warnings.formatwarning = custom_warning
[docs]class AdsorbateOperator(OffspringCreator):
"""Base class for all operators that add, move or remove adsorbates.
Don't use this operator directly!"""
def __init__(self, adsorbate_species, species_probabilities=None, num_muts=1):
OffspringCreator.__init__(self, num_muts=num_muts)
self.adsorbate_species = adsorbate_species if is_list_or_tuple(
adsorbate_species) else [adsorbate_species]
self.species_probabilities = species_probabilities
if self.species_probabilities is not None:
assert len(self.species_probabilities.keys()) == len(self.adsorbate_species)
self.descriptor = 'AdsorbateOperator'
[docs] @classmethod
def initialize_individual(cls, parent, indi=None):
indi = OffspringCreator.initialize_individual(parent, indi=indi)
if 'unrelaxed_adsorbates' in parent.info['data']:
unrelaxed = list(parent.info['data']['unrelaxed_adsorbates'])
else:
unrelaxed = []
indi.info['data']['unrelaxed_adsorbates'] = unrelaxed
return indi
[docs] def get_new_individual(self, parents):
raise NotImplementedError
[docs] def add_adsorbate(self, atoms, hetero_site_list, heights,
adsorbate_species=None,
min_adsorbate_distance=2.,
tilt_angle=0.):
"""Adds the adsorbate in self.adsorbate to the supplied atoms
object at the first free site in the specified site_list. A site
is free if no other adsorbates can be found in a sphere of radius
min_adsorbate_distance around the chosen site.
Parameters
----------
atoms : ase.Atoms object
The atoms object that the adsorbate will be added to.
hetero_site_list : list
A list of dictionaries, each dictionary contains site
information given by acat.adsorbate_coverage module.
heights : dict
A dictionary that contains the adsorbate height for each site
type.
adsorbate_species : str or list of strs, default None
One or a list of adsorbate species to be added to the surface.
Use self.adsorbate_species if not specified.
min_adsorbate_distance : float, default 2.
The radius of the sphere inside which no other adsorbates
should be found.
tilt_angle : float, default 0.
Tilt the adsorbate with an angle (in degrees) relative to the
surface normal.
"""
if adsorbate_species is None:
adsorbate_species = self.adsorbate_species
elif not is_list_or_tuple(adsorbate_species):
adsorbate_species = [adsorbate_species]
i = 0
too_close = True
while too_close:
if i >= len(hetero_site_list):
return False
site = hetero_site_list[i]
if site['occupied']:
i += 1
continue
# Allow only single-atom species to enter subsurf 6fold sites
this_site = site['site']
if this_site == '6fold':
adsorbate_species = [s for s in adsorbate_species if len(s) == 1]
# Add a random adsorbate to the correct position
if self.species_probabilities is None:
adsorbate = random.choice(adsorbate_species)
else:
probs = [self.species_probabilities[a] for a in adsorbate_species]
adsorbate = random.choices(k=1, population=adsorbate_species,
weights=probs)[0]
height = heights[this_site]
atoms = atoms.copy()
add_adsorbate_to_site(atoms, adsorbate, site, height,
tilt_angle=tilt_angle)
nads = len(list(Formula(adsorbate)))
ads_atoms = atoms[[a.index for a in atoms if
a.symbol in adsorbate_elements]]
if atoms_too_close_after_addition(ads_atoms, nads,
cutoff=min_adsorbate_distance, mic=(True in atoms.pbc)):
atoms = atoms[:-nads]
i += 1
continue
too_close = False
# Setting the indices of the unrelaxed adsorbates for the cut-
# relax-paste function to be executed in the calculation script.
# There it should also reset the parameter to [], to indicate
# that the adsorbates have been relaxed.
ads_indices = sorted([len(atoms) - k - 1 for k in range(nads)])
if 'unrelaxed_adsorbates' not in atoms.info['data']:
atoms.info['data']['unrelaxed_adsorbates'] = []
atoms.info['data']['unrelaxed_adsorbates'].append(ads_indices)
return atoms
[docs] def remove_adsorbate(self, atoms, hetero_site_list, return_site_index=False):
"""Removes an adsorbate from the atoms object at the first occupied
site in hetero_site_list. If no adsorbates can be found, one will be
added instead.
Parameters
----------
atoms : ase.Atoms object
The atoms object that the adsorbate will be added to
hetero_site_list : list
A list of dictionaries, each dictionary contains site
information given by acat.adsorbate_coverage module.
return_site_index : bool, default False
Whether to return the site index of the hetero_site_list instead
of the site. Useful for moving or replacing adsorbate.
"""
i = 0
occupied = False
while not occupied:
if i >= len(hetero_site_list):
if return_site_index:
return False
warnings.warn('Removal not possible, will add instead')
return self.add_adsorbate(atoms, hetero_site_list, site_heights)
site = hetero_site_list[i]
if not site['occupied']:
i += 1
continue
# Remove adsorbate from the correct position. Remove the fragment
# with priority if it is one of the given adsorbate species
rm_frag = (site['fragment'] in self.adsorbate_species)
remove_adsorbate_from_site(atoms, site, remove_fragment=rm_frag)
if return_site_index:
return i
occupied = True
return atoms
def get_all_adsorbate_indices(self, atoms):
from asap3 import FullNeighborList
ac = atoms.copy()
ads_ind = [a.index for a in ac if
a.symbol in adsorbate_elements]
mbl = 1.5 # max_bond_length
nl = FullNeighborList(rCut=mbl / 2., atoms=ac)
adsorbates = []
while len(ads_ind) != 0:
i = int(ads_ind[0])
mol_ind = self._get_indices_in_adsorbate(ac, nl, i)
for ind in mol_ind:
if int(ind) in ads_ind:
ads_ind.remove(int(ind))
adsorbates.append(sorted(mol_ind))
return adsorbates
[docs] def get_adsorbate_indices(self, atoms, position):
"""Returns the indices of the adsorbate at the supplied position."""
dmin = 1000.
for a in atoms:
if a.symbol in adsorbate_elements:
d = np.linalg.norm(a.position - position)
if d < dmin:
dmin = d
ind = a.index
for ads in self.get_all_adsorbate_indices(atoms):
if ind in ads:
return ads[:]
def _get_indices_in_adsorbate(self, atoms, neighborlist,
index, molecule_indices=None):
"""Internal recursive function that help
determine adsorbate indices."""
if molecule_indices is None:
molecule_indices = []
mi = molecule_indices
nl = neighborlist
mi.append(index)
# neighbors, _ = nl.get_neighbors(index)
neighbors = nl[index]
for n in neighbors:
if int(n) not in mi:
if atoms[int(n)].symbol in adsorbate_elements:
mi = self._get_indices_in_adsorbate(atoms, nl, n, mi)
return mi
[docs]class AddAdsorbate(AdsorbateOperator):
"""
Use this operator to add adsorbates to the surface.
The surface is allowed to change during the algorithm run.
There is no limit of adsorbate species. You can either provide one
species or a list of species.
Site and surface preference can be supplied. If both are supplied site
will be considered first.
Supplying a tilt angle will tilt the adsorbate with an angle relative
to the standard perpendicular to the surface.
The operator is generalized for both periodic and non-periodic systems
(distinguished by atoms.pbc).
Parameters
----------
adsorbate_species : str or list of strs
One or a list of adsorbate species to be added to the surface.
species_probabilities : dict, default None
A dictionary that contains keys of each adsorbate species and
values of their probabilities of adding onto the surface.
Adding adsorbate species with equal probability if not specified.
max_species : int, default None
The maximum allowed adsorbate species (excluding vacancies) for a
single structure. Allow all adsorbate species if not specified.
heights : dict, default acat.settings.site_heights
A dictionary that contains the adsorbate height for each site
type. Use the default height settings if the height for a site
type is not specified.
min_adsorbate_distance : float, default 2.
The radius of the sphere inside which no other adsorbates
should be found.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites object or acat.adsorption_sites.SlabAdsorptionSites object or the corresponding pickle file path, default None
Provide the built-in adsorption sites class to accelerate the
genetic algorithm. Make sure all the operators used with this
operator preserve the indexing of the atoms.
site_preference : str or list of strs, defualt None
The site type(s) that has higher priority to attach adsorbates.
surface_preference : str, default None
The surface type that has higher priority to attach adsorbates.
Please only use this for nanoparticles.
max_coverage : float, default None
The maximum allowed coverage on the surface. Coverage is defined
as (number of surface occupied sites / number of surface atoms).
The maximum coverage is solely governed by min_adsorbate_distance
if max_coverage is not specified.
tilt_angle : float, default 0.
Tilt the adsorbate with an angle (in degrees) relative to the
surface normal.
subtract_heights : bool, default False
Whether to subtract the height from the bond length when allocating
a site to an adsorbate. Default is to allocate the site that is
closest to the adsorbate's binding atom without subtracting height.
Useful for ensuring the allocated site for each adsorbate is
consistent with the site to which the adsorbate was added.
num_muts : int, default 1
The number of times to perform this operation.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between an atom and its
nearest site to be considered as the atom being bound to the site.
"""
def __init__(self, adsorbate_species,
species_probabilities=None,
max_species=None,
heights=site_heights,
min_adsorbate_distance=2.,
adsorption_sites=None,
site_preference=None,
surface_preference=None,
max_coverage=None,
tilt_angle=None,
subtract_heights=False,
num_muts=1,
dmax=2.5, **kwargs):
AdsorbateOperator.__init__(self, adsorbate_species,
species_probabilities,
num_muts=num_muts)
self.descriptor = 'AddAdsorbate'
if max_species is None:
self.max_species = None
else:
self.max_species = min([max_species, len(set(self.adsorbate_species))])
self.heights = site_heights
for k, v in heights.items():
self.heights[k] = v
self.min_adsorbate_distance = min_adsorbate_distance
self.kwargs = {'allow_6fold': False, 'composition_effect': False,
'ignore_sites': None, 'label_sites': False}
self.kwargs.update(kwargs)
if adsorption_sites is not None:
if isinstance(adsorption_sites, str):
import pickle
with open(adsorption_sites, 'rb') as f:
self.adsorption_sites = pickle.load(f)
else:
self.adsorption_sites = adsorption_sites
for k in self.kwargs.keys():
self.kwargs[k] = attrgetter(k)(self.adsorption_sites)
else:
self.adsorption_sites = None
self.__dict__.update(self.kwargs)
self.site_preference = site_preference
if site_preference is not None:
if not is_list_or_tuple(site_preference):
self.site_preference = [site_preference]
self.surface_preference = surface_preference
self.max_coverage = max_coverage
self.tilt_angle = tilt_angle or 0.
if subtract_heights:
self.subtract_heights = self.heights
else:
self.subtract_heights = None
self.min_inputs = 1
self.dmax = dmax
[docs] def get_new_individual(self, parents):
"""Returns the new individual as an atoms object."""
f = parents[0]
indi = self.initialize_individual(f)
indi.info['data']['parents'] = [f.info['confid']]
for atom in f:
indi.append(atom)
if True in indi.pbc:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = SlabAdsorptionSites(indi, **self.kwargs)
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = ClusterAdsorptionSites(indi, **self.kwargs)
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
if self.max_species is None:
options = self.adsorbate_species
else:
occupied_specs = list({st['adsorbate'] for st in ads_sites if st['occupied']})
diff = self.max_species - len(occupied_specs)
if diff == 0:
options = occupied_specs
elif diff > 0:
others = [sp for sp in self.adsorbate_species if sp not in occupied_specs]
if self.species_probabilities is None:
dspecs = random.sample(others, diff)
else:
probs = [self.species_probabilities[osp] for osp in others]
dspecs = random.choices(k=diff, population=others, weights=probs)
options = occupied_specs + dspecs
else:
options = occupied_specs
warnings.warn('The number of adsorbate species exceeds ' +
'the maximum allowed number.')
for i in range(self.num_muts):
# Make sure the coverage does not exceed maximum coverage
if self.max_coverage is not None:
if sac.coverage >= self.max_coverage:
if i == 0:
warnings.warn('Addition not possible, will remove instead')
indi = self.remove_adsorbate(indi, ads_sites)
break
random.shuffle(ads_sites)
if self.surface_preference is not None:
def func(x):
return x['surface'] == self.surface_preference
ads_sites.sort(key=func, reverse=True)
if self.site_preference is not None:
def func(x):
return x['site'] in self.site_preference
ads_sites.sort(key=func, reverse=True)
nindi = self.add_adsorbate(indi, ads_sites, self.heights,
options, self.min_adsorbate_distance,
tilt_angle=self.tilt_angle)
if not nindi:
warnings.warn('Addition not possible, will remove instead')
indi = self.remove_adsorbate(indi, ads_sites)
break
indi = nindi
if True in indi.pbc:
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
indi.info['data']['adsorbates'] = [t[0] for t in
sac.get_adsorbates(self.adsorbate_species)]
return (self.finalize_individual(indi),
self.descriptor + ': {0}'.format(f.info['confid']))
[docs]class RemoveAdsorbate(AdsorbateOperator):
"""This operator removes an adsorbate from the surface. It works
exactly (but doing the opposite) as the AddAdsorbate operator.
The operator is generalized for both periodic and non-periodic systems
(distinguished by atoms.pbc).
Parameters
----------
adsorbate_species : str or list of strs
One or a list of adsorbate species to be removed from the surface.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites object or acat.adsorption_sites.SlabAdsorptionSites object or the corresponding pickle file path, default None
Provide the built-in adsorption sites class to accelerate the
genetic algorithm. Make sure all the operators used with this
operator preserve the indexing of the atoms.
site_preference : str or list of strs, defualt None
The site type(s) that has higher priority to detach adsorbates.
surface_preference : str, default None
The surface type that has higher priority to detach adsorbates.
Please only use this for nanoparticles.
min_coverage : float, default None
The minimum allowed coverage on the surface. Coverage is defined
as (number of surface occupied sites / number of surface atoms).
The minimum coverage is 0 if min_coverage is not specified.
subtract_heights : bool, default False
Whether to subtract the height from the bond length when allocating
a site to an adsorbate. Default is to allocate the site that is
closest to the adsorbate's binding atom without subtracting height.
Useful for ensuring the allocated site for each adsorbate is
consistent with the site to which the adsorbate was added.
num_muts : int, default 1
The number of times to perform this operation.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between an atom and its
nearest site to be considered as the atom being bound to the site.
"""
def __init__(self, adsorbate_species,
adsorption_sites=None,
site_preference=None,
surface_preference=None,
min_coverage=None,
subtract_heights=False,
num_muts=1,
dmax=2.5, **kwargs):
AdsorbateOperator.__init__(self, adsorbate_species,
num_muts=num_muts)
self.descriptor = 'RemoveAdsorbate'
self.kwargs = {'allow_6fold': False, 'composition_effect': False,
'ignore_sites': None, 'label_sites': False}
self.kwargs.update(kwargs)
if adsorption_sites is not None:
if isinstance(adsorption_sites, str):
import pickle
with open(adsorption_sites, 'rb') as f:
self.adsorption_sites = pickle.load(f)
else:
self.adsorption_sites = adsorption_sites
for k in self.kwargs.keys():
self.kwargs[k] = attrgetter(k)(self.adsorption_sites)
else:
self.adsorption_sites = None
self.__dict__.update(self.kwargs)
self.site_preference = site_preference
if site_preference is not None:
if not is_list_or_tuple(site_preference):
self.site_preference = [site_preference]
self.min_coverage = min_coverage
self.surface_preference = surface_preference
if subtract_heights:
self.subtract_heights = self.heights
else:
self.subtract_heights = None
self.min_inputs = 1
self.dmax = dmax
[docs] def get_new_individual(self, parents):
f = parents[0]
indi = self.initialize_individual(f)
indi.info['data']['parents'] = [f.info['confid']]
for atom in f:
indi.append(atom)
if True in indi.pbc:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = SlabAdsorptionSites(indi, **self.kwargs)
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = ClusterAdsorptionSites(indi, **self.kwargs)
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
for _ in range(self.num_muts):
# Make sure the coverage is not lower than the minimum coverage
if self.min_coverage is not None:
if sac.coverage <= self.min_coverage:
break
random.shuffle(ads_sites)
if self.surface_preference is not None:
def func(x):
return x['surface'] == self.surface_preference
ads_sites.sort(key=func, reverse=True)
if self.site_preference is not None:
def func(x):
return x['site'] in self.site_preference
ads_sites.sort(key=func, reverse=True)
indi = self.remove_adsorbate(indi, ads_sites)
if True in indi.pbc:
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
# Make sure there are still adsorbates to remove
if not any(st for st in ads_sites if st['occupied']):
break
indi.info['data']['adsorbates'] = [t[0] for t in
sac.get_adsorbates(self.adsorbate_species)]
return (self.finalize_individual(indi),
self.descriptor + ': {0}'.format(f.info['confid']))
[docs]class MoveAdsorbate(AdsorbateOperator):
"""This operator removes an adsorbate from the surface and adds it
again to a different site, i.e. effectively moving the adsorbate.
The operator is generalized for both periodic and non-periodic systems
(distinguished by atoms.pbc).
Parameters
----------
adsorbate_species : str or list of strs
One or a list of adsorbate species to be added to the surface.
heights : dict, default acat.settings.site_heights
A dictionary that contains the adsorbate height for each site
type. Use the default height settings if the height for a site
type is not specified.
min_adsorbate_distance : float, default 2.
The radius of the sphere inside which no other adsorbates
should be found.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites object or acat.adsorption_sites.SlabAdsorptionSites object or the corresponding pickle file path, default None
Provide the built-in adsorption sites class to accelerate the
genetic algorithm. Make sure all the operators used with this
operator preserve the indexing of the atoms.
site_preference_from : str or list of strs, defualt None
The site type(s) that has higher priority to detach adsorbates.
surface_preference_from : str, default None
The surface type that has higher priority to detach adsorbates.
Please only use this for nanoparticles.
site_preference_to : str or list of strs, defualt None
The site type(s) that has higher priority to attach adsorbates.
surface_preference_to : str, default None
The surface type that has higher priority to attach adsorbates.
Please only use this for nanoparticles.
tilt_angle : float, default 0.
Tilt the adsorbate with an angle (in degrees) relative to the
surface normal.
subtract_heights : bool, default False
Whether to subtract the height from the bond length when allocating
a site to an adsorbate. Default is to allocate the site that is
closest to the adsorbate's binding atom without subtracting height.
Useful for ensuring the allocated site for each adsorbate is
consistent with the site to which the adsorbate was added.
num_muts : int, default 1
The number of times to perform this operation.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between an atom and its
nearest site to be considered as the atom being bound to the site.
"""
def __init__(self, adsorbate_species,
heights=site_heights,
min_adsorbate_distance=2.,
adsorption_sites=None,
site_preference_from=None,
surface_preference_from=None,
site_preference_to=None,
surface_preference_to=None,
tilt_angle=None,
subtract_heights=False,
num_muts=1,
dmax=2.5, **kwargs):
AdsorbateOperator.__init__(self, adsorbate_species,
num_muts=num_muts)
self.descriptor = 'MoveAdsorbate'
self.heights = site_heights
for k, v in heights.items():
self.heights[k] = v
self.min_adsorbate_distance = min_adsorbate_distance
self.kwargs = {'allow_6fold': False, 'composition_effect': False,
'ignore_sites': None, 'label_sites': False}
self.kwargs.update(kwargs)
if adsorption_sites is not None:
if isinstance(adsorption_sites, str):
import pickle
with open(adsorption_sites, 'rb') as f:
self.adsorption_sites = pickle.load(f)
else:
self.adsorption_sites = adsorption_sites
for k in self.kwargs.keys():
self.kwargs[k] = attrgetter(k)(self.adsorption_sites)
else:
self.adsorption_sites = None
self.__dict__.update(self.kwargs)
self.site_preference_from = site_preference_from
if site_preference_from is not None:
if not is_list_or_tuple(site_preference_from):
self.site_preference_from = [site_preference_from]
self.surface_preference_from = surface_preference_from
self.site_preference_to = site_preference_to
if site_preference_to is not None:
if not is_list_or_tuple(site_preference_to):
self.site_preference_to = [site_preference_to]
self.surface_preference_to = surface_preference_to
self.tilt_angle = tilt_angle or 0.
if subtract_heights:
self.subtract_heights = self.heights
else:
self.subtract_heights = None
self.min_inputs = 1
self.dmax = dmax
[docs] def get_new_individual(self, parents):
f = parents[0]
indi = self.initialize_individual(f)
indi.info['data']['parents'] = [f.info['confid']]
for atom in f:
indi.append(atom)
if True in indi.pbc:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = SlabAdsorptionSites(indi, **self.kwargs)
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = ClusterAdsorptionSites(indi, **self.kwargs)
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
for _ in range(self.num_muts):
random.shuffle(ads_sites)
if self.surface_preference_from is not None:
def func(x):
return x['surface'] == self.surface_preference_from
ads_sites.sort(key=func, reverse=True)
if self.site_preference_from is not None:
def func(x):
return x['site'] in self.site_preference_from
ads_sites.sort(key=func, reverse=True)
removed = self.remove_adsorbate(indi, ads_sites,
return_site_index=True)
if removed is False:
removed_species = random.choice(self.adsorbate_species)
else:
removed_species = ads_sites[removed]['fragment']
random.shuffle(ads_sites)
if self.surface_preference_to is not None:
def func(x):
return x['surface'] == self.surface_preference_to
ads_sites.sort(key=func, reverse=True)
if self.site_preference_to is not None:
def func(x):
return x['site'] in self.site_preference_to
ads_sites.sort(key=func, reverse=True)
nindi = self.add_adsorbate(indi, ads_sites,
self.heights, removed_species,
self.min_adsorbate_distance,
tilt_angle=self.tilt_angle)
if not nindi:
break
indi = nindi
if True in indi.pbc:
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
indi.info['data']['adsorbates'] = [t[0] for t in
sac.get_adsorbates(self.adsorbate_species)]
return (self.finalize_individual(indi),
self.descriptor + ': {0}'.format(f.info['confid']))
[docs]class ReplaceAdsorbate(AdsorbateOperator):
"""This operator removes an adsorbate from the surface and adds another
species to the same site, i.e. effectively replacing the adsorbate.
The operator is generalized for both periodic and non-periodic systems
(distinguished by atoms.pbc).
Parameters
----------
adsorbate_species : str or list of strs
One or a list of adsorbate species to be added to the surface.
species_probabilities : dict, default None
A dictionary that contains keys of each adsorbate species and
values of their probabilities of replacing an adsorbate on the
surface. Choosing adsorbate species with equal probability if
not specified.
max_species : int, default None
The maximum allowed adsorbate species (excluding vacancies) for a
single structure. Allow all adsorbate species if not specified.
heights : dict, default acat.settings.site_heights
A dictionary that contains the adsorbate height for each site
type. Use the default height settings if the height for a site
type is not specified.
min_adsorbate_distance : float, default 2.
The radius of the sphere inside which no other adsorbates
should be found.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites object or acat.adsorption_sites.SlabAdsorptionSites object or the corresponding pickle file path, default None
Provide the built-in adsorption sites class to accelerate the
genetic algorithm. Make sure all the operators used with this
operator preserve the indexing of the atoms.
site_preference : str or list of strs, defualt None
The site type(s) that has higher priority to replace adsorbates.
surface_preference : str, default None
The surface type that has higher priority to replace adsorbates.
Please only use this for nanoparticles.
tilt_angle : float, default 0.
Tilt the adsorbate with an angle (in degrees) relative to the
surface normal.
subtract_heights : bool, default False
Whether to subtract the height from the bond length when allocating
a site to an adsorbate. Default is to allocate the site that is
closest to the adsorbate's binding atom without subtracting height.
Useful for ensuring the allocated site for each adsorbate is
consistent with the site to which the adsorbate was added.
num_muts : int, default 1
The number of times to perform this operation.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between an atom and its
nearest site to be considered as the atom being bound to the site.
"""
def __init__(self, adsorbate_species,
species_probabilities=None,
max_species=None,
heights=site_heights,
min_adsorbate_distance=2.,
adsorption_sites=None,
site_preference=None,
surface_preference=None,
tilt_angle=None,
subtract_heights=False,
num_muts=1,
dmax=2.5, **kwargs):
AdsorbateOperator.__init__(self, adsorbate_species,
species_probabilities,
num_muts=num_muts)
self.descriptor = 'ReplaceAdsorbate'
if max_species is None:
self.max_species = None
else:
self.max_species = min([max_species, len(set(self.adsorbate_species))])
self.heights = site_heights
for k, v in heights.items():
self.heights[k] = v
self.min_adsorbate_distance = min_adsorbate_distance
self.kwargs = {'allow_6fold': False, 'composition_effect': False,
'ignore_sites': None, 'label_sites': False}
self.kwargs.update(kwargs)
if adsorption_sites is not None:
if isinstance(adsorption_sites, str):
import pickle
with open(adsorption_sites, 'rb') as f:
self.adsorption_sites = pickle.load(f)
else:
self.adsorption_sites = adsorption_sites
for k in self.kwargs.keys():
self.kwargs[k] = attrgetter(k)(self.adsorption_sites)
else:
self.adsorption_sites = None
self.__dict__.update(self.kwargs)
self.site_preference = site_preference
if site_preference is not None:
if not is_list_or_tuple(site_preference):
self.site_preference = [site_preference]
self.surface_preference = surface_preference
self.tilt_angle = tilt_angle or 0.
if subtract_heights:
self.subtract_heights = self.heights
else:
self.subtract_heights = None
self.min_inputs = 1
self.dmax = dmax
[docs] def get_new_individual(self, parents):
f = parents[0]
indi = self.initialize_individual(f)
indi.info['data']['parents'] = [f.info['confid']]
for atom in f:
indi.append(atom)
if True in indi.pbc:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = SlabAdsorptionSites(indi, **self.kwargs)
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = ClusterAdsorptionSites(indi, **self.kwargs)
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
for _ in range(self.num_muts):
random.shuffle(ads_sites)
if self.surface_preference is not None:
def func(x):
return x['surface'] == self.surface_preference
ads_sites.sort(key=func, reverse=True)
if self.site_preference is not None:
def func(x):
return x['site'] in self.site_preference
ads_sites.sort(key=func, reverse=True)
removed = self.remove_adsorbate(indi, ads_sites,
return_site_index=True)
if removed is False:
removed = random.choice(range(len(ads_sites)))
removed_species = 'x'
else:
removed_species = ads_sites[removed]['fragment']
ads_sites[removed]['occupied'] = 0
other_specs = [s for s in self.adsorbate_species if
s != removed_species]
if self.max_species is None:
options = other_specs
else:
occupied_specs = list({st['adsorbate'] for st in ads_sites if st['occupied']})
diff = self.max_species - len(occupied_specs)
if diff == 0:
options = [s for s in occupied_specs if s in other_specs]
assert len(options) > 0
elif diff > 0:
options = other_specs
else:
options = other_specs
warnings.warn('The number of adsorbate species exceeds ' +
'the maximum allowed number.')
nindi = self.add_adsorbate(indi, [ads_sites[removed]],
self.heights, options,
self.min_adsorbate_distance,
tilt_angle=self.tilt_angle)
if not nindi:
break
indi = nindi
if True in indi.pbc:
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
indi.info['data']['adsorbates'] = [t[0] for t in
sac.get_adsorbates(self.adsorbate_species)]
return (self.finalize_individual(indi),
self.descriptor + ': {0}'.format(f.info['confid']))
[docs]class ReplaceAdsorbateSpecies(AdsorbateOperator):
"""This operator replace all adsorbates of a certain species with
another species at the same sites. Return None if there is no
adsorbate present on the surface.
The operator is generalized for both periodic and non-periodic systems
(distinguished by atoms.pbc).
Parameters
----------
adsorbate_species : str or list of strs
One or a list of adsorbate species to be added to the surface.
species_probabilities : dict, default None
A dictionary that contains keys of each adsorbate species and
values of their probabilities of replacing an adsorbate on the
surface. Choosing adsorbate species with equal probability if
not specified.
replace_vacancy : bool, default False
Whether to allow replacing adsorbates with vacancies, i.e.,
effectively removing all adsorbates of a certain species.
Note that if you want to specify species_probabilties, you
then need to also provide the probability for vacancy
replacement using the keyword 'vacancy'.
heights : dict, default acat.settings.site_heights
A dictionary that contains the adsorbate height for each site
type. Use the default height settings if the height for a site
type is not specified.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites object or acat.adsorption_sites.SlabAdsorptionSites object or the corresponding pickle file path, default None
Provide the built-in adsorption sites class to accelerate the
genetic algorithm. Make sure all the operators used with this
operator preserve the indexing of the atoms.
subtract_heights : bool, default False
Whether to subtract the height from the bond length when allocating
a site to an adsorbate. Default is to allocate the site that is
closest to the adsorbate's binding atom without subtracting height.
Useful for ensuring the allocated site for each adsorbate is
consistent with the site to which the adsorbate was added.
tilt_angle : float, default 0.
Tilt the adsorbate with an angle (in degrees) relative to the
surface normal.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between an atom and its
nearest site to be considered as the atom being bound to the site.
"""
def __init__(self, adsorbate_species,
replace_vacancy=False,
species_probabilities=None,
heights=site_heights,
adsorption_sites=None,
tilt_angle=None,
subtract_heights=False,
dmax=2.5, **kwargs):
AdsorbateOperator.__init__(self, adsorbate_species,
species_probabilities)
self.descriptor = 'ReplaceAdsorbateSpecies'
assert len(set(self.adsorbate_species)) > 1
self.replace_vacancy = replace_vacancy
self.heights = site_heights
for k, v in heights.items():
self.heights[k] = v
self.kwargs = {'allow_6fold': False, 'composition_effect': False,
'ignore_sites': None, 'label_sites': False}
self.kwargs.update(kwargs)
if adsorption_sites is not None:
if isinstance(adsorption_sites, str):
import pickle
with open(adsorption_sites, 'rb') as f:
self.adsorption_sites = pickle.load(f)
else:
self.adsorption_sites = adsorption_sites
for k in self.kwargs.keys():
self.kwargs[k] = attrgetter(k)(self.adsorption_sites)
else:
self.adsorption_sites = None
self.__dict__.update(self.kwargs)
self.tilt_angle = tilt_angle or 0.
if subtract_heights:
self.subtract_heights = self.heights
else:
self.subtract_heights = None
self.min_inputs = 1
self.dmax = dmax
[docs] def get_new_individual(self, parents):
f = parents[0]
indi = self.initialize_individual(f)
indi.info['data']['parents'] = [f.info['confid']]
if 'groups' in f.info['data']:
indi.info['data']['groups'] = f.info['data']['groups']
for atom in f:
indi.append(atom)
if True in indi.pbc:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = SlabAdsorptionSites(indi, **self.kwargs)
sac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
if self.adsorption_sites is not None:
sas = deepcopy(self.adsorption_sites)
sas.update(indi)
else:
sas = ClusterAdsorptionSites(indi, **self.kwargs)
sac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = sac.hetero_site_list
specs = [t[0] for t in sac.get_adsorbates(self.adsorbate_species)]
# Return None if no adsorbate is present
if not specs:
return None, '{0} not possible in {1}'.format(self.descriptor,
f.info['confid'])
spec = random.choice(specs)
all_specs = self.adsorbate_species.copy()
if self.replace_vacancy:
if 'vacancy' not in all_specs:
all_specs.append('vacancy')
other_specs = [sp for sp in all_specs if sp != spec]
if self.species_probabilities is None:
to_spec = random.choice(other_specs)
else:
probs = []
for osp in other_specs:
if (osp == 'vacancy') and ('vacancy' not in self.species_probabilities):
vs = self.species_probabilities.values()
prob = sum(vs) / len(vs)
else:
prob = self.species_probabilities[osp]
probs.append(prob)
to_spec = random.choices(k=1, population=other_specs, weights=probs)[0]
rmsites, rmstids = [], []
rmfrags = True
for i, st in enumerate(ads_sites):
if st['fragment'] in self.adsorbate_species:
if st['fragment'] == spec:
rmsites.append(st)
rmstids.append(i)
else:
if st['adsorbate'] == spec:
rmsites.append(st)
rmstids.append(i)
rmfrags = False
remove_adsorbates_from_sites(indi, sites=rmsites, remove_fragments=rmfrags)
if to_spec != 'vacancy':
for i, st in enumerate(ads_sites):
if i in rmstids:
height = self.heights[st['site']]
# height = st['bond_length']
add_adsorbate_to_site(indi, to_spec, st, height, tilt_angle=self.tilt_angle)
if True in indi.pbc:
nsac = SlabAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
else:
nsac = ClusterAdsorbateCoverage(indi, sas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
indi.info['data']['adsorbates'] = [t[0] for t in
nsac.get_adsorbates(self.adsorbate_species)]
return (self.finalize_individual(indi),
self.descriptor + ': {0}'.format(f.info['confid']))
[docs]class CutSpliceCrossoverWithAdsorbates(AdsorbateOperator):
"""Crossover that cuts two particles with adsorbates through a plane
in space and merges two halfes from different particles together
(only returns one of them). The indexing of the atoms is not
preserved. Please only use this operator if the particle is allowed
to change shape.
It keeps the correct composition by randomly assigning elements in
the new particle. If some of the atoms in the two particle halves
are too close, the halves are moved away from each other perpendicular
to the cutting plane.
The complexity of crossover with adsorbates makes this operator not
robust enough. The adsorption site identification will fail once the
nanoparticle shape becomes too irregular after crossover.
Parameters
----------
adsorbate_species : str or list of strs
One or a list of adsorbate species to be added to the surface.
blmin : dict
Dictionary of minimum distance between atomic numbers.
e.g. {(28,29): 1.5}
heights : dict, default acat.settings.site_heights
A dictionary that contains the adsorbate height for each site
type. Use the default height settings if the height for a site
type is not specified.
keep_composition : bool, default True
Should the composition be the same as in the parents.
fix_coverage : bool, default False
Should the adsorbate coverage be the same as in the parents.
min_adsorbate_distance : float, default 2.
The radius of the sphere inside which no other adsorbates
should be found.
rotate_vectors : list, default None
A list of vectors that the part of the structure that is cut
is able to rotate around, the size of rotation is set in
rotate_angles. Default None meaning no rotation is performed.
rotate_angles : list, default None
A list of angles that the structure cut can be rotated. The
vector being rotated around is set in rotate_vectors. Default
None meaning no rotation is performed.
subtract_heights : bool, default False
Whether to subtract the height from the bond length when allocating
a site to an adsorbate. Default is to allocate the site that is
closest to the adsorbate's binding atom without subtracting height.
Useful for ensuring the allocated site for each adsorbate is
consistent with the site to which the adsorbate was added.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between an atom and its
nearest site to be considered as the atom being bound to the site.
"""
def __init__(self, adsorbate_species, blmin,
heights=site_heights,
keep_composition=True,
fix_coverage=False,
min_adsorbate_distance=2.,
rotate_vectors=None,
rotate_angles=None,
subtract_heights=False,
dmax=2.5, **kwargs):
AdsorbateOperator.__init__(self, adsorbate_species)
self.descriptor = 'CutSpliceCrossoverWithAdsorbates'
self.blmin = blmin
self.heights = site_heights
for k, v in heights.items():
self.heights[k] = v
self.keep_composition = keep_composition
self.fix_coverage = fix_coverage
self.min_adsorbate_distance = min_adsorbate_distance
self.rvecs = rotate_vectors
self.rangs = rotate_angles
if subtract_heights:
self.subtract_heights = self.heights
else:
self.subtract_heights = None
self.min_inputs = 2
self.dmax = dmax
self.kwargs = {'allow_6fold': False, 'composition_effect': False,
'ignore_sites': None, 'label_sites': False}
self.kwargs.update(kwargs)
self.__dict__.update(self.kwargs)
[docs] def get_new_individual(self, parents):
f, m = parents
if self.fix_coverage:
# Count number of adsorbates
adsorbates_in_parents = len(self.get_all_adsorbate_indices(f))
indi = self.initialize_individual(f)
indi.info['data']['parents'] = [i.info['confid'] for i in parents]
fna = self.get_atoms_without_adsorbates(f)
mna = self.get_atoms_without_adsorbates(m)
fna_geo_mid = np.average(fna.get_positions(), 0)
mna_geo_mid = np.average(mna.get_positions(), 0)
if self.rvecs is not None:
if not isinstance(self.rvecs, list):
warnings.warn('Rotation vectors are not a list, skipping rotation')
else:
vec = random.choice(self.rvecs)
try:
angle = random.choice(self.rangs)
except TypeError:
angle = self.rangs
f.rotate(angle, vec, center=fna_geo_mid)
vec = random.choice(self.rvecs)
try:
angle = random.choice(self.rangs)
except TypeError:
angle = self.rangs
m.rotate(angle, vec, center=mna_geo_mid)
theta = random.random() * 2 * np.pi # 0,2pi
phi = random.random() * np.pi # 0,pi
e = np.asarray((np.sin(phi) * np.cos(theta),
np.sin(theta) * np.sin(phi),
np.cos(phi)))
eps = 0.0001
# Move each particle to origo with their respective geometrical
# centers, without adsorbates
common_mid = (fna_geo_mid + mna_geo_mid) / 2.
f.translate(-common_mid)
m.translate(-common_mid)
off = 1
while off != 0:
fna = self.get_atoms_without_adsorbates(f)
mna = self.get_atoms_without_adsorbates(m)
# Get the signed distance to the cutting plane
# We want one side from f and the other side from m
fmap = [np.dot(x, e) for x in fna.get_positions()]
mmap = [-np.dot(x, e) for x in mna.get_positions()]
ain = sorted([i for i in chain(fmap, mmap) if i > 0],
reverse=True)
aout = sorted([i for i in chain(fmap, mmap) if i < 0],
reverse=True)
off = len(ain) - len(fna)
# Translating f and m to get the correct number of atoms
# in the offspring
if off < 0:
# too few
# move f and m away from the plane
dist = abs(aout[abs(off) - 1]) + eps
f.translate(e * dist)
m.translate(-e * dist)
elif off > 0:
# too many
# move f and m towards the plane
dist = abs(ain[-abs(off)]) + eps
f.translate(-e * dist)
m.translate(e * dist)
eps /= 5.
fna = self.get_atoms_without_adsorbates(f)
mna = self.get_atoms_without_adsorbates(m)
# Determine the contributing parts from f and m
tmpf, tmpm = Atoms(), Atoms()
for atom in fna:
if np.dot(atom.position, e) > 0:
atom.tag = 1
tmpf.append(atom)
for atom in mna:
if np.dot(atom.position, e) < 0:
atom.tag = 2
tmpm.append(atom)
# Place adsorbates from f and m in tmpf and tmpm
f_ads = self.get_all_adsorbate_indices(f)
m_ads = self.get_all_adsorbate_indices(m)
for ads in f_ads:
if np.dot(f[ads[0]].position, e) > 0:
for i in ads:
f[i].tag = 1
tmpf.append(f[i])
for ads in m_ads:
pos = m[ads[0]].position
if np.dot(pos, e) < 0:
# If the adsorbate will sit too close to another adsorbate
# (below self.min_adsorbate_distance) do not add it.
dists = [np.linalg.norm(pos - a.position)
for a in tmpf if a.tag == 1]
for d in dists:
if d < self.min_adsorbate_distance:
break
else:
for i in ads:
m[i].tag = 2
tmpm.append(m[i])
tmpfna = self.get_atoms_without_adsorbates(tmpf)
tmpmna = self.get_atoms_without_adsorbates(tmpm)
# Check that the correct composition is employed
if self.keep_composition:
opt_sm = sorted(fna.numbers)
tmpf_numbers = list(tmpfna.numbers)
tmpm_numbers = list(tmpmna.numbers)
cur_sm = sorted(tmpf_numbers + tmpm_numbers)
# correct_by: dictionary that specifies how many
# of the atom_numbers should be removed (a negative number)
# or added (a positive number)
correct_by = dict([(j, opt_sm.count(j)) for j in set(opt_sm)])
for n in cur_sm:
correct_by[n] -= 1
correct_in = random.choice([tmpf, tmpm])
to_add, to_rem = [], []
for num, amount in correct_by.items():
if amount > 0:
to_add.extend([num] * amount)
elif amount < 0:
to_rem.extend([num] * abs(amount))
for add, rem in zip(to_add, to_rem):
tbc = [a.index for a in correct_in if a.number == rem]
if len(tbc) == 0:
pass
ai = random.choice(tbc)
correct_in[ai].number = add
# Move the contributing apart if any distance is below blmin
maxl = 0.
for sv, min_dist in self.get_vectors_below_min_dist(tmpf + tmpm):
lsv = np.linalg.norm(sv) # length of shortest vector
d = [-np.dot(e, sv)] * 2
d[0] += np.sqrt(np.dot(e, sv)**2 - lsv**2 + min_dist**2)
d[1] -= np.sqrt(np.dot(e, sv)**2 - lsv**2 + min_dist**2)
l = sorted([abs(i) for i in d])[0] / 2. + eps
if l > maxl:
maxl = l
tmpf.translate(e * maxl)
tmpm.translate(-e * maxl)
# Translate particles halves back to the center
tmpf.translate(common_mid)
tmpm.translate(common_mid)
# Put the two parts together
for atom in chain(tmpf, tmpm):
indi.append(atom)
pcas = ClusterAdsorptionSites(indi, **self.kwargs)
pcac = ClusterAdsorbateCoverage(indi, pcas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
pads_sites = pcac.hetero_site_list
adsi_dict = {}
for st in pads_sites:
if st['occupied']:
si = st['indices']
adsi_dict[si] = {}
adsi_dict[si]['height'] = self.heights[st['site']]
adsi_dict[si]['fragment'] = st['fragment']
adsi_dict[si]['fragment_indices'] = st['fragment_indices']
indi = pcas.atoms
indi.positions = pcas.ref_atoms.positions
cas = ClusterAdsorptionSites(indi, **self.kwargs)
nori = len(indi)
for st in cas.site_list:
si = st['indices']
if si in adsi_dict:
adsorbate = adsi_dict[si]['fragment']
height = adsi_dict[si]['height']
add_adsorbate_to_site(indi, adsorbate, st, height)
# Make sure no adsorbates too close to each other
# after each adsorbate addition
nads = len(adsi_dict[si]['fragment_indices'])
ads_atoms = indi[[a.index for a in indi if
a.symbol in adsorbate_elements]]
if atoms_too_close_after_addition(ads_atoms, nads,
self.min_adsorbate_distance, mic=False):
indi = indi[:-nads]
# Add adsorbate if no adsorbate is present
if len(indi) == nori:
st = random.choice(cas.site_list)
ads_spec = random.choice(self.adsorbate_species)
add_adsorbate_to_site(indi, ads_spec, site=st,
height=self.heights[st['site']])
cac = ClusterAdsorbateCoverage(indi, cas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = cac.hetero_site_list
if self.fix_coverage:
# Remove or add adsorbates as needed
adsorbates_in_child = self.get_all_adsorbate_indices(indi)
diff = len(adsorbates_in_child) - adsorbates_in_parents
if diff < 0:
# Add adsorbates
for _ in range(abs(diff)):
self.add_adsorbate(indi, ads_sites, site_heights,
self.adsorbate_species,
self.min_adsorbate_distance)
elif diff > 0:
# Remove adsorbates
tbr = random.sample(adsorbates_in_child, diff) # to be removed
for adsorbate_indices in sorted(tbr, reverse=True):
for i in adsorbate_indices[::-1]:
indi.pop(i)
cac = ClusterAdsorbateCoverage(indi, cas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
indi.info['data']['adsorbates'] = [t[0] for t in
cac.get_adsorbates(self.adsorbate_species)]
return (self.finalize_individual(indi),
self.descriptor + ': {0} {1}'.format(f.info['confid'],
m.info['confid']))
def get_numbers(self, atoms):
"""Returns the atomic numbers of the atoms object
without adsorbates"""
ac = atoms.copy()
del ac[[a.index for a in ac
if a.symbol in adsorbate_elements]]
return ac.numbers
def get_atoms_without_adsorbates(self, atoms):
ac = atoms.copy()
del ac[[a.index for a in ac
if a.symbol in adsorbate_elements]]
return ac
[docs] def get_vectors_below_min_dist(self, atoms):
"""Generator function that returns each vector (between atoms)
that is shorter than the minimum distance for those atom types
(set during the initialization in blmin)."""
ap = atoms.get_positions()
an = atoms.numbers
for i in range(len(atoms)):
pos = atoms[i].position
for j, d in enumerate([np.linalg.norm(k - pos) for k in ap[i:]]):
if d == 0:
continue
min_dist = self.blmin[tuple(sorted((an[i], an[j + i])))]
if d < min_dist:
yield atoms[i].position - atoms[j + i].position, min_dist
[docs]class SimpleCutSpliceCrossoverWithAdsorbates(AdsorbateOperator):
"""Crossover that divides two particles through a plane in space and
merges the symbols of two halves from different particles with
adosrbates together (only returns one of them). The indexing of the
atoms is preserved. Please only use this operator with other operators
that also preserves the indexing.
It keeps the correct composition by randomly assigning elements in
the new particle.
Parameters
----------
adsorbate_species : str or list of strs
One or a list of adsorbate species to be added to the surface.
heights : dict, default acat.settings.site_heights
A dictionary that contains the adsorbate height for each site
type. Use the default height settings if the height for a site
type is not specified.
keep_composition : bool, default True
Boolean that signifies if the composition should be the same
as in the parents.
fix_coverage : bool, default False
Should the adsorbate coverage be the same as in the parents.
min_adsorbate_distance : float, default 2.
The radius of the sphere inside which no other adsorbates
should be found.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites object or acat.adsorption_sites.SlabAdsorptionSites object or the corresponding pickle file path, default None
Provide the built-in adsorption sites class to accelerate the
genetic algorithm. Make sure all the operators used with this
operator preserve the indexing of the atoms.
subtract_heights : bool, default False
Whether to subtract the height from the bond length when allocating
a site to an adsorbate. Default is to allocate the site that is
closest to the adsorbate's binding atom without subtracting height.
Useful for ensuring the allocated site for each adsorbate is
consistent with the site to which the adsorbate was added.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between an atom and its
nearest site to be considered as the atom being bound to the site.
"""
def __init__(self, adsorbate_species,
heights=site_heights,
keep_composition=True,
fix_coverage=False,
min_adsorbate_distance=2.,
adsorption_sites=None,
subtract_heights=False,
dmax=2.5, **kwargs):
AdsorbateOperator.__init__(self, adsorbate_species)
self.descriptor = 'SimpleCutSpliceCrossoverWithAdsorbates'
self.heights = site_heights
for k, v in heights.items():
self.heights[k] = v
self.keep_composition = keep_composition
self.fix_coverage = fix_coverage
self.min_adsorbate_distance = min_adsorbate_distance
self.kwargs = {'allow_6fold': False, 'composition_effect': False,
'ignore_sites': None, 'label_sites': False}
self.kwargs.update(kwargs)
if adsorption_sites is not None:
if isinstance(adsorption_sites, str):
import pickle
with open(adsorption_sites, 'rb') as f:
self.adsorption_sites = pickle.load(f)
else:
self.adsorption_sites = adsorption_sites
for k in self.kwargs.keys():
self.kwargs[k] = attrgetter(k)(self.adsorption_sites)
else:
self.adsorption_sites = None
self.__dict__.update(self.kwargs)
if subtract_heights:
self.subtract_heights = self.heights
else:
self.subtract_heights = None
self.min_inputs = 2
self.dmax = dmax
[docs] def get_new_individual(self, parents):
f, m = parents
indi = f.copy()
if self.fix_coverage:
# Count number of adsorbates
adsorbates_in_parents = len(self.get_all_adsorbate_indices(f))
theta = random.random() * 2 * np.pi # 0,2pi
phi = random.random() * np.pi # 0,pi
e = np.array((np.sin(phi) * np.cos(theta),
np.sin(theta) * np.sin(phi),
np.cos(phi)))
eps = 0.0001
f.translate(-f.get_center_of_mass())
m.translate(-m.get_center_of_mass())
# Get the signed distance to the cutting plane
# We want one side from f and the other side from m
mids, fids, rmids = [], [], []
for i, x in enumerate(f.get_positions()):
if np.dot(x, e) > 0:
if f[i].symbol in adsorbate_elements:
rmids.append(i)
else:
mids.append(i)
else:
if f[i].symbol not in adsorbate_elements:
fids.append(i)
# Change half of f symbols to the half of m symbols
for i in mids:
indi[i].symbol = m[i].symbol
# Check that the correct composition is employed
if self.keep_composition:
opt_sm = sorted([a.number for a in f if a.symbol
not in adsorbate_elements])
tmpf_numbers = list(indi.numbers[fids])
tmpm_numbers = list(indi.numbers[mids])
cur_sm = sorted(tmpf_numbers + tmpm_numbers)
# correct_by: dictionary that specifies how many
# of the atom_numbers should be removed (a negative number)
# or added (a positive number)
correct_by = dict([(j, opt_sm.count(j)) for j in set(opt_sm)])
for n in cur_sm:
correct_by[n] -= 1
correct_ids = random.choice([fids, mids])
to_add, to_rem = [], []
for num, amount in correct_by.items():
if amount > 0:
to_add.extend([num] * amount)
elif amount < 0:
to_rem.extend([num] * abs(amount))
for add, rem in zip(to_add, to_rem):
tbc = [i for i in correct_ids if indi[i].number == rem]
if len(tbc) == 0:
pass
ai = random.choice(tbc)
indi[ai].number = add
# Place adsorbates from half of m and remove adsorbates from
# half of f
if self.adsorption_sites is not None:
cas = deepcopy(self.adsorption_sites)
cas.update(indi)
else:
cas = ClusterAdsorptionSites(indi, **self.kwargs)
fcac = ClusterAdsorbateCoverage(indi, cas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
fhsl = fcac.hetero_site_list
mcac = ClusterAdsorbateCoverage(m, cas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
mhsl = mcac.hetero_site_list
rmset = set(rmids)
rmsites = []
for st in fhsl:
if st['occupied']:
if not set(st['fragment_indices']).isdisjoint(rmset):
rmsites.append(st)
# Remove fragments if every fragment is one of the given adsorbate species
rmfrags = all(st['fragment'] in self.adsorbate_species for st in rmsites)
remove_adsorbates_from_sites(indi, sites=rmsites, remove_fragments=rmfrags)
mset = set(mids)
adsi_dict = {}
for st in mhsl:
if st['occupied']:
si = st['indices']
if set(si).issubset(mset):
si = st['indices']
adsi_dict[si] = {}
adsi_dict[si]['height'] = self.heights[st['site']]
adsi_dict[si]['fragment'] = st['fragment']
adsi_dict[si]['fragment_indices'] = st['fragment_indices']
nori = len(indi)
for st in cas.site_list:
si = st['indices']
if si in adsi_dict:
adsorbate = adsi_dict[si]['fragment']
height = adsi_dict[si]['height']
add_adsorbate_to_site(indi, adsorbate, st, height)
# Make sure no adsorbates too close to each other
# after each adsorbate addition
nads = len(adsi_dict[si]['fragment_indices'])
ads_atoms = indi[[a.index for a in indi if
a.symbol in adsorbate_elements]]
if atoms_too_close_after_addition(ads_atoms, nads,
self.min_adsorbate_distance, mic=False):
indi = indi[:-nads]
# Add adsorbate if no adsorbate is present
if len(indi) == nori:
st = random.choice(cas.site_list)
ads_spec = random.choice(self.adsorbate_species)
add_adsorbate_to_site(indi, ads_spec, site=st,
height=self.heights[st['site']])
cac = ClusterAdsorbateCoverage(indi, cas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
ads_sites = cac.hetero_site_list
if self.fix_coverage:
# Remove or add adsorbates as needed
adsorbates_in_child = self.get_all_adsorbate_indices(indi)
diff = len(adsorbates_in_child) - adsorbates_in_parents
if diff < 0:
# Add adsorbates
for _ in range(abs(diff)):
self.add_adsorbate(indi, ads_sites, site_heights,
self.adsorbate_species,
self.min_adsorbate_distance)
elif diff > 0:
# Remove adsorbates
tbr = random.sample(adsorbates_in_child, diff) # to be removed
for adsorbate_indices in sorted(tbr, reverse=True):
for i in adsorbate_indices[::-1]:
indi.pop(i)
cac = ClusterAdsorbateCoverage(indi, cas, subtract_heights=
self.subtract_heights, dmax=self.dmax)
indi = self.initialize_individual(f, indi)
indi.info['data']['parents'] = [i.info['confid'] for i in parents]
indi.info['data']['operation'] = 'crossover'
indi.info['data']['adsorbates'] = [t[0] for t in
cac.get_adsorbates(self.adsorbate_species)]
parent_message = ':Parents {0} {1}'.format(f.info['confid'],
m.info['confid'])
return (self.finalize_individual(indi),
self.descriptor + parent_message)
def get_numbers(self, atoms):
"""Returns the atomic numbers of the atoms object
without adsorbates"""
ac = atoms.copy()
del ac[[a.index for a in ac
if a.symbol in adsorbate_elements]]
return ac.numbers
def get_atoms_without_adsorbates(self, atoms):
ac = atoms.copy()
del ac[[a.index for a in ac
if a.symbol in adsorbate_elements]]
return ac
[docs]class CatalystAdsorbateCrossover(AdsorbateOperator):
"""Crossover that divides two particles or two slabs by the catalyst-
adsorbates interfaces and exchange all adsorbates (only returns one
of them). The indexing of the atoms is preserved. Please only use
this operator with other operators that also preserves the indexing.
The composition or the coverage is fixed if it is preserved by all
other operators being used.
Parameters
----------
group_by_sites : bool, default False
If atoms.info['data']['groups'] is used, please set this to True
if the groups is for adsorption sites, so that the offspring will
inheritate the site groups. The default is grouping by slab atoms.
catalyst_indices : list of ints, default None
The atomic indices of catalyst atoms. Only metal atoms are treated
as part of the catalyst by default. Useful when the indexing is
preserved during the run, and there are non-metal elements in the
catalyst, e.g. metal oxides.
"""
def __init__(self, group_by_sites=False, catalyst_indices=None):
AdsorbateOperator.__init__(self, adsorbate_species='X')
self.descriptor = 'CatalystAdsorbateCrossover'
self.group_by_sites = group_by_sites
self.catalyst_indices = catalyst_indices
self.min_inputs = 2
[docs] def get_new_individual(self, parents):
f, m = parents
indi = f.copy()
if self.catalyst_indices is None:
cat_ids = [a.index for a in indi if a.symbol not in adsorbate_elements]
else:
cat_ids = self.catalyst_indices
indi.symbols[cat_ids] = m.symbols[cat_ids]
indi = self.initialize_individual(f, indi)
if 'groups' in f.info['data']:
if self.group_by_sites:
indi.info['data']['groups'] = f.info['data']['groups']
else:
indi.info['data']['groups'] = m.info['data']['groups']
if 'adsorbates' in f.info['data']:
indi.info['data']['adsorbates'] = f.info['data']['adsorbates']
indi.info['data']['parents'] = [i.info['confid'] for i in parents]
indi.info['data']['operation'] = 'crossover'
parent_message = ':Parents {0} {1}'.format(f.info['confid'],
m.info['confid'])
return (self.finalize_individual(indi),
self.descriptor + parent_message)