from .settings import adsorbate_elements, adsorbate_formulas
from .adsorption_sites import ClusterAdsorptionSites, SlabAdsorptionSites
from .utilities import string_fragmentation, neighbor_shell_list, get_connectivity_matrix
from ase.data import atomic_numbers
from ase.geometry import find_mic
from ase.formula import Formula
from collections import defaultdict, Counter
from operator import itemgetter
from copy import deepcopy
import networkx as nx
import numpy as np
[docs]class ClusterAdsorbateCoverage(object):
"""Child class of `ClusterAdsorptionSites` for identifying adsorbate
coverage on a nanoparticle. Support common nanoparticle shapes
including: Mackay icosahedron, (truncated) octahedron and (Marks)
decahedron.
The information of each occupied site stored in the dictionary is
updated with the following new keys:
**'occupied'**: 1 if the site is occupied, otherwise 0.
**'adsorbate'**: the name of the adsorbate that occupies this site.
**'adsorbate_indices'**: the indices of the adosorbate atoms that occupy
this site. If the adsorbate is multidentate, these atoms might
occupy multiple sites.
**'bonding_index'**: the index of the atom that directly bonds to the
site (closest to the site).
**'fragment'**: the name of the fragment that occupies this site. Useful
for multidentate species.
**'fragment_indices'**: the indices of the fragment atoms that occupy
this site. Useful for multidentate species.
**'bond_length'**: the distance between the bonding atom and the site.
**'dentate'**: dentate number.
**'label'**: the updated label with the name of the occupying adsorbate
if label_occupied_sites is set to True.
Parameters
----------
atoms : ase.Atoms object
The atoms object must be a non-periodic nanoparticle with at
least one adsorbate attached to it. Accept any ase.Atoms object.
No need to be built-in.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites object, \
default None
`ClusterAdsorptionSites` object of the nanoparticle. Initialize a
`ClusterAdsorptionSites` object if not specified.
label_occupied_sites : bool, default False
Whether to assign a label to the occupied each site. The string
of the occupying adsorbate is concatentated to the numerical
label that represents the occpied site. Since the site labelling
currently only supports monometallics and bimetallics, the
numerical part will all be 0 if there are more than 2 metal
components.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between the site and the
bonding atom that should be considered as an adsorbate.
Example
-------
The following example illustrates the most important use of a
`ClusterAdsorbateCoverage` object - getting occupied adsorption sites:
>>> from acat.adsorption_sites import ClusterAdsorptionSites
>>> from acat.adsorbate_coverage import ClusterAdsorbateCoverage
>>> from acat.build.actions import add_adsorbate_to_site
>>> from ase.cluster import Octahedron
>>> atoms = Octahedron('Ni', length=7, cutoff=2)
>>> for atom in atoms:
... if atom.index % 2 == 0:
... atom.symbol = 'Pt'
>>> atoms.center(vacuum=5.)
>>> cas = ClusterAdsorptionSites(atoms, composition_effect=True)
>>> sites = cas.get_sites()
>>> for s in sites:
... if s['site'] == 'fcc':
... add_adsorbate_to_site(atoms, adsorbate='CO', site=s)
>>> cac = ClusterAdsorbateCoverage(atoms, adsorption_sites=cas,
... label_occupied_sites=True)
>>> occupied_sites = cac.get_sites(occupied_only=True)
>>> print(occupied_sites[0])
Output:
.. code-block:: python
{'site': 'fcc', 'surface': 'fcc111',
'position': array([ 6.41470446, 8.17470446, 11.69470446]),
'normal': array([-0.57735027, -0.57735027, -0.57735027]),
'indices': (0, 2, 4), 'composition': 'PtPtPt',
'subsurf_index': None, 'subsurf_element': None, 'label': '21CO',
'bonding_index': 201, 'bond_length': 1.3000000000000007,
'adsorbate': 'CO', 'fragment': 'CO', 'adsorbate_indices': (201, 202),
'occupied': 1, 'dentate': 1, 'fragment_indices': (201, 202)}
"""
def __init__(self, atoms,
adsorption_sites=None,
label_occupied_sites=False,
dmax=2.5):
atoms = atoms.copy()
for dim in range(3):
if np.linalg.norm(atoms.cell[dim]) == 0:
atoms.cell[dim][dim] = np.ptp(atoms.positions[:, dim]) + 10.
self.atoms = atoms
self.positions = atoms.positions
self.symbols = atoms.symbols
self.ads_ids = [a.index for a in atoms if
a.symbol in adsorbate_elements]
assert self.ads_ids, 'cannot find any adsorbate'
self.ads_atoms = atoms[self.ads_ids]
self.cell = atoms.cell
self.pbc = atoms.pbc
self.label_occupied_sites = label_occupied_sites
self.dmax = dmax
self.make_ads_neighbor_list()
self.ads_connectivity_matrix = self.get_ads_connectivity()
self.identify_adsorbates()
if adsorption_sites:
cas = adsorption_sites
else:
cas = ClusterAdsorptionSites(atoms, allow_6fold=True,
composition_effect=True)
self.cas = cas
self.slab_ids = cas.indices
self.allow_6fold = cas.allow_6fold
self.composition_effect = cas.composition_effect
self.metals = cas.metals
if len(self.metals) == 1 and self.composition_effect:
self.metals *= 2
self.surf_ids = cas.surf_ids
self.label_dict = cas.label_dict
self.hetero_site_list = deepcopy(cas.site_list)
self.label_list = ['0'] * len(self.hetero_site_list)
self.populate_occupied_sites()
self.labels = self.get_occupied_labels()
def identify_adsorbates(self):
G = nx.Graph()
adscm = self.ads_connectivity_matrix
# Cut all intermolecular H-H bonds except intramolecular
# H-H bonds in e.g. H2
hids = [a.index for a in self.ads_atoms if a.symbol == 'H']
for hi in hids:
conns = np.where(adscm[hi] == 1)[0]
hconns = [i for i in conns if self.ads_atoms.symbols[i] == 'H']
if hconns and len(conns) > 1:
adscm[hi,hconns] = 0
if adscm.size != 0:
np.fill_diagonal(adscm, 1)
rows, cols = np.where(adscm == 1)
edges = zip([self.ads_ids[row] for row in rows.tolist()],
[self.ads_ids[col] for col in cols.tolist()])
G.add_edges_from(edges)
SG = (G.subgraph(c) for c in nx.connected_components(G))
adsorbates = []
for sg in SG:
nodes = sorted(list(sg.nodes))
adsorbates += [nodes]
else:
adsorbates = [self.ads_ids]
self.ads_list = adsorbates
[docs] def get_hetero_connectivity(self):
"""Get the connection matrix of slab + adsorbates."""
nbslist = neighbor_shell_list(self.atoms, 0.3, neighbor_number=1)
return get_connectivity_matrix(nbslist)
[docs] def get_ads_connectivity(self):
"""Get the connection matrix for adsorbate atoms."""
return get_connectivity_matrix(self.ads_nblist)
[docs] def get_site_connectivity(self):
"""Get the connection matrix for adsorption sites."""
sl = self.hetero_site_list
conn_mat = []
for i, sti in enumerate(sl):
conn_x = []
for j, stj in enumerate(sl):
overlap = len(set(sti['indices']).intersection(stj['indices']))
if i == j:
conn_x.append(0.)
elif overlap > 0:
if self.allow_6fold:
if '6fold' in [sti['site'], stj['site']]:
if overlap == 3:
conn_x.append(1.)
else:
conn_x.append(0.)
else:
conn_x.append(1.)
else:
conn_x.append(1.)
else:
conn_x.append(0.)
conn_mat.append(conn_x)
return np.asarray(conn_mat)
[docs] def populate_occupied_sites(self):
"""Find all the occupied sites, identify the adsorbate coverage
of those sites and collect in a heterogeneous site list."""
hsl = self.hetero_site_list
ll = self.label_list
ads_list = self.ads_list
ndentate_dict = {}
for adsid in self.ads_ids:
if self.symbols[adsid] == 'H':
if [adsid] not in ads_list:
rest = [s for x in ads_list for s in x
if (adsid in x and s != adsid)]
if not (self.symbols[rest[0]] == 'H' and len(rest) == 1):
continue
adspos = self.positions[adsid]
bls = np.linalg.norm(np.asarray([s['position'] -
adspos for s in hsl]), axis=1)
stid, bl = min(enumerate(bls), key=itemgetter(1))
st = hsl[stid]
if bl > self.dmax:
continue
adsids = next((l for l in ads_list if adsid in l), None)
adsi = tuple(sorted(adsids))
if 'occupied' in st:
if bl >= st['bond_length']:
continue
elif self.symbols[adsid] != 'H':
if adsi in ndentate_dict:
ndentate_dict[adsi] -= 1
st['bonding_index'] = adsid
st['bond_length'] = bl
symbols = ''.join(list(Formula(str(self.symbols[adsids]))))
adssym = next((k for k, v in adsorbate_formulas.items()
if v == symbols), None)
if adssym is None:
adssym = next((k for k, v in adsorbate_formulas.items()
if sorted(v) == sorted(symbols)), symbols)
st['adsorbate'] = adssym
st['fragment'] = adssym
st['adsorbate_indices'] = adsi
if adsi in ndentate_dict:
ndentate_dict[adsi] += 1
else:
ndentate_dict[adsi] = 1
st['occupied'] = 1
# Get dentate numbers and coverage
self.n_occupied, n_surf_occupied, self.n_subsurf_occupied = 0, 0, 0
for st in hsl:
if 'occupied' not in st:
st['bonding_index'] = st['bond_length'] = None
st['adsorbate'] = st['fragment'] = None
st['adsorbate_indices'] = None
st['occupied'] = st['dentate'] = 0
st['fragment_indices'] = None
st['label'] = 0
continue
self.n_occupied += 1
if st['site'] == '6fold':
self.n_subsurf_occupied += 1
else:
n_surf_occupied += 1
adsi = st['adsorbate_indices']
if adsi in ndentate_dict:
st['dentate'] = ndentate_dict[adsi]
else:
st['dentate'] = 0
self.coverage = n_surf_occupied / len(self.surf_ids)
# Identify bidentate fragments and assign labels
self.multidentate_fragments = []
self.monodentate_adsorbate_list = []
self.multidentate_labels = []
multidentate_adsorbate_dict = {}
for j, st in enumerate(hsl):
adssym = st['adsorbate']
if st['occupied']:
if st['dentate'] > 1:
bondid = st['bonding_index']
bondsym = self.symbols[bondid]
fsym = next((f for f in string_fragmentation(adssym)
if f[0] == bondsym), None)
st['fragment'] = fsym
flen = len(list(Formula(fsym)))
adsids = st['adsorbate_indices']
ibond = adsids.index(bondid)
fsi = adsids[ibond:ibond+flen]
st['fragment_indices'] = fsi
if adsids not in multidentate_adsorbate_dict:
multidentate_adsorbate_dict[adsids] = adssym
else:
st['fragment_indices'] = st['adsorbate_indices']
self.monodentate_adsorbate_list.append(adssym)
if self.label_occupied_sites:
if len(self.metals) > 2:
stlab = 0
else:
if st['label'] is None:
signature = [st['site'], st['surface']]
if self.composition_effect:
signature.append(st['composition'])
stlab = self.label_dict['|'.join(signature)]
else:
stlab = st['label']
label = str(stlab) + st['fragment']
st['label'] = label
ll[j] = label
if st['dentate'] > 1:
self.multidentate_fragments.append(label)
if bondid == adsids[0]:
mdlabel = str(stlab) + adssym
self.multidentate_labels.append(mdlabel)
self.multidentate_adsorbate_list = list(multidentate_adsorbate_dict.values())
self.adsorbate_list = self.monodentate_adsorbate_list + \
self.multidentate_adsorbate_list
[docs] def get_site(self, indices):
"""Get information of a site given its atom indices.
Parameters
----------
indices : list or tuple
The indices of the atoms that contribute to the site.
"""
indices = indices if is_list_or_tuple(indices) else [indices]
indices = tuple(sorted(indices))
st = next((s for s in self.hetero_site_list if
s['indices'] == indices), None)
return st
[docs] def get_sites(self, occupied_only=False):
"""Get information of all sites.
Parameters
----------
occupied_only : bool, default False
Whether to only return occupied sites.
"""
all_sites = self.hetero_site_list
if occupied_only:
all_sites = [s for s in all_sites if s['occupied']]
return all_sites
[docs] def get_adsorbates(self):
"""Get a list of tuples that contains each adsorbate (string)
and the corresponding adsorbate indices (tuple)."""
adsorbates = []
adsorbate_ids = set()
for st in self.hetero_site_list:
if st['occupied']:
adsi = st['adsorbate_indices']
if not set(adsi).issubset(adsorbate_ids):
adsorbates.append((st['adsorbate'], adsi))
adsorbate_ids.update(adsi)
return adsorbates
def make_ads_neighbor_list(self, dx=.2, neighbor_number=1):
self.ads_nblist = neighbor_shell_list(self.ads_atoms, dx,
neighbor_number, mic=False)
[docs] def get_occupied_labels(self, fragmentation=True):
"""Get a list of labels of all occupied sites. The label consists
of a numerical part that represents site, and a character part
that represents the occupying adsorbate.
Parameters
----------
fragmentation : bool, default True
Whether to cut multidentate species into fragments. This ensures
that multidentate species with different orientations have
different labels.
"""
if not self.label_occupied_sites:
return self.atoms[self.ads_ids].get_chemical_formula(mode='hill')
ll = self.label_list
labs = [lab for lab in ll if lab != '0']
if not fragmentation:
mf = self.multidentate_fragments
mdlabs = self.multidentate_labels
c1, c2 = Counter(labs), Counter(mf)
diff = list((c1 - c2).elements())
labs = diff + mdlabs
return sorted(labs)
[docs] def get_graph(self, fragmentation=True, subsurf_effect=False):
"""Get the graph representation of the nanoparticle with adsorbates.
Parameters
----------
fragmentation : bool, default True
Whether to cut multidentate species into fragments. This ensures
that multidentate species with different orientations have
different graphs.
subsurf_effect : bool, default False
Take subsurface atoms into consideration when genearting graph.
"""
hsl = self.hetero_site_list
hcm = self.cas.get_connectivity().copy()
if subsurf_effect:
surf_ids = self.surf_ids + self.cas.get_subsurface()
else:
surf_ids = self.surf_ids
surfhcm = hcm[surf_ids]
symbols = self.symbols[surf_ids]
nrows, ncols = surfhcm.shape
newrows, frag_list = [], []
for st in hsl:
if st['occupied']:
if not fragmentation and st['dentate'] > 1:
if st['bonding_index'] != st['adsorbate_indices'][0]:
continue
si = st['indices']
newrow = np.zeros(ncols)
newrow[list(si)] = 1
newrows.append(newrow)
if fragmentation:
frag_list.append(st['fragment'])
else:
frag_list.append(st['adsorbate'])
if newrows:
surfhcm = np.vstack((surfhcm, np.asarray(newrows)))
G = nx.Graph()
# Add nodes from fragment list
G.add_nodes_from([(i, {'symbol': symbols[i]})
for i in range(nrows)] +
[(j + nrows, {'symbol': frag_list[j]})
for j in range(len(frag_list))])
# Add edges from surface connectivity matrix
shcm = surfhcm[:,surf_ids]
shcm = shcm * np.tri(*shcm.shape, k=-1)
rows, cols = np.where(shcm == 1)
edges = zip(rows.tolist(), cols.tolist())
G.add_edges_from(edges)
return G
[docs] def get_coverage(self):
"""Get the adsorbate coverage (ML) of the surface."""
return self.coverage
[docs] def get_subsurf_coverage(self):
"""Get the adsorbate coverage (ML) of the subsurface."""
nsubsurf = len(self.cas.get_subsurface())
return self.n_subsurf_occupied / nsubsurf
[docs]class SlabAdsorbateCoverage(object):
"""Child class of `SlabAdsorptionSites` for identifying adsorbate
coverage on a surface slab. Support 20 common surfaces: fcc100,
fcc111, fcc110, fcc211, fcc221, fcc311, fcc322, fcc331, fcc332,
bcc100, bcc111, bcc110, bcc210, bcc211, bcc310, hcp0001,
hcp10m10t, hcp10m10h, hcp10m11, hcp10m12.
The information of each occupied site stored in the dictionary is
updated with the following new keys:
**'occupied'**: 1 if the site is occupied, otherwise 0.
**'adsorbate'**: the name of the adsorbate that occupies this site.
**'adsorbate_indices'**: the indices of the adosorbate atoms that occupy
this site. If the adsorbate is multidentate, these atoms might
occupy multiple sites.
**'bonding_index'**: the index of the atom that directly bonds to the
site (closest to the site).
**'fragment'**: the name of the fragment that occupies this site. Useful
for multidentate species.
**'fragment_indices'**: the indices of the fragment atoms that occupy
this site. Useful for multidentate species.
**'bond_length'**: the distance between the bonding atom and the site.
**'dentate'**: dentate number.
**'label'**: the updated label with the name of the occupying adsorbate
if label_occupied_sites is set to True.
Parameters
----------
atoms : ase.Atoms object
The atoms object must be a non-periodic nanoparticle with at
least one adsorbate attached to it. Accept any ase.Atoms object.
No need to be built-in.
adsorption_sites : acat.adsorption_sites.SlabAdsorptionSites object, \
default None
`SlabAdsorptionSites` object of the nanoparticle. Initialize a
`SlabAdsorptionSites` object if not specified.
surface : str
The surface type (crystal structure + Miller indices).
Required if adsorption_sites is not provided.
both_sides : bool, default False
Whether to consider adsorbate coverage on both top and bottom
sides of the slab. Only works if adsorption_sites is not provided,
otherwise consistent with what is specified in adsorption_sites.
label_occupied_sites : bool, default False
Whether to assign a label to the occupied each site. The string
of the occupying adsorbate is concatentated to the numerical
label that represents the occpied site. Since the site labelling
currently only supports monometallics and bimetallics, the
numerical part will all be 0 if there are more than 2 metal
components.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between the site and the
bonding atom that should be considered as an adsorbate.
Example
-------
The following example illustrates the most important use of a
`SlabAdsorbateCoverage` object - getting occupied adsorption sites:
>>> from acat.adsorption_sites import SlabAdsorptionSites
>>> from acat.adsorbate_coverage import SlabAdsorbateCoverage
>>> from acat.build.actions import add_adsorbate
>>> from ase.build import fcc211
>>> atoms = fcc211('Cu', (3, 3, 4), vacuum=5.)
>>> for atom in atoms:
... if atom.index % 2 == 0:
... atom.symbol = 'Au'
>>> atoms.center()
>>> add_adsorbate(atoms, adsorbate='CH3OH', surface='fcc211',
... indices=(5, 7, 8))
>>> sac = SlabAdsorbateCoverage(atoms, surface='fcc211',
... label_occupied_sites=True)
>>> occupied_sites = sac.get_sites(occupied_only=True)
>>> print(occupied_sites[0])
Output:
.. code-block:: python
{'site': 'bridge', 'surface': 'fcc211', 'morphology': 'tc-cc-h',
'position': array([ 2.08423447, 3.82898322, 12.00043756]),
'normal': array([-0.33333333, 0. , 0.94280904]),
'indices': (4, 7), 'composition': 'CuAu', 'subsurf_index': None,
'subsurf_element': None, 'label': '17OH', 'bonding_index': 40,
'bond_length': 1.4378365786909804, 'adsorbate': 'CH3OH',
'fragment': 'OH', 'adsorbate_indices': (36, 37, 38, 39, 40, 41),
'occupied': 1, 'dentate': 2, 'fragment_indices': (40, 41)}
"""
def __init__(self, atoms,
adsorption_sites=None,
surface=None,
both_sides=False,
label_occupied_sites=False,
dmax=2.5):
atoms = atoms.copy()
ptp = np.ptp(atoms.positions[:, 2])
if np.linalg.norm(atoms.cell[2]) - ptp < 10.:
atoms.cell[2][2] = ptp + 10.
self.atoms = atoms
self.positions = atoms.positions
self.symbols = atoms.symbols
self.ads_ids = [a.index for a in atoms if
a.symbol in adsorbate_elements]
assert self.ads_ids, 'cannot find any adsorbate'
self.ads_atoms = atoms[self.ads_ids]
self.cell = atoms.cell
self.pbc = atoms.pbc
self.both_sides = both_sides
self.label_occupied_sites = label_occupied_sites
self.dmax = dmax
self.make_ads_neighbor_list()
self.ads_connectivity_matrix = self.get_ads_connectivity()
self.identify_adsorbates()
if adsorption_sites:
sas = adsorption_sites
else:
sas = SlabAdsorptionSites(atoms, surface,
allow_6fold=True,
composition_effect=True,
both_sides=both_sides)
self.sas = sas
self.slab_ids = sas.indices
self.surface = sas.surface
self.allow_6fold = sas.allow_6fold
self.composition_effect = sas.composition_effect
self.both_sides = sas.both_sides
self.metals = sas.metals
if len(self.metals) == 1 and self.composition_effect:
self.metals *= 2
self.surf_ids = sas.surf_ids
self.subsurf_ids = sas.subsurf_ids
self.connectivity_matrix = sas.connectivity_matrix
self.label_dict = sas.label_dict
self.hetero_site_list = deepcopy(sas.site_list)
self.label_list = ['0'] * len(self.hetero_site_list)
self.populate_occupied_sites()
self.labels = self.get_occupied_labels()
def identify_adsorbates(self):
G = nx.Graph()
adscm = self.ads_connectivity_matrix
# Cut all intermolecular H-H bonds except intramolecular
# H-H bonds in e.g. H2
hids = [a.index for a in self.ads_atoms if a.symbol == 'H']
for hi in hids:
conns = np.where(adscm[hi] == 1)[0]
hconns = [i for i in conns if self.ads_atoms.symbols[i] == 'H']
if hconns and len(conns) > 1:
adscm[hi,hconns] = 0
if adscm.size != 0:
np.fill_diagonal(adscm, 1)
rows, cols = np.where(adscm == 1)
edges = zip([self.ads_ids[row] for row in rows.tolist()],
[self.ads_ids[col] for col in cols.tolist()])
G.add_edges_from(edges)
SG = (G.subgraph(c) for c in nx.connected_components(G))
adsorbates = []
for sg in SG:
nodes = sorted(list(sg.nodes))
adsorbates += [nodes]
else:
adsorbates = [self.ads_ids]
self.ads_list = adsorbates
[docs] def get_hetero_connectivity(self):
"""Get the connection matrix of slab + adsorbates."""
nbslist = neighbor_shell_list(self.atoms, 0.3, neighbor_number=1)
return get_connectivity_matrix(nbslist)
[docs] def get_ads_connectivity(self):
"""Get the connection matrix for adsorbate atoms."""
return get_connectivity_matrix(self.ads_nblist)
[docs] def get_site_connectivity(self):
"""Get the connection matrix for adsorption sites."""
sl = self.hetero_site_list
conn_mat = []
for i, sti in enumerate(sl):
conn_x = []
for j, stj in enumerate(sl):
overlap = len(set(sti['indices']).intersection(stj['indices']))
if i == j:
conn_x.append(0.)
elif overlap > 0:
if self.allow_6fold:
if 'subsurf' in [sti['morphology'], stj['morphology']]:
if overlap == 3:
conn_x.append(1.)
else:
conn_x.append(0.)
else:
conn_x.append(1.)
else:
conn_x.append(1.)
else:
conn_x.append(0.)
conn_mat.append(conn_x)
return np.asarray(conn_mat)
[docs] def populate_occupied_sites(self):
"""Find all the occupied sites, identify the adsorbate coverage
of those sites and collect in a heterogeneous site list."""
hsl = self.hetero_site_list
ll = self.label_list
ads_list = self.ads_list
ndentate_dict = {}
for adsid in self.ads_ids:
if self.symbols[adsid] == 'H':
if [adsid] not in ads_list:
rest = [s for x in ads_list for s in x
if (adsid in x and s != adsid)]
if not (self.symbols[rest[0]] == 'H' and len(rest) == 1):
continue
adspos = self.positions[adsid]
_, bls = find_mic(np.asarray([s['position'] - adspos for s in hsl]),
cell=self.cell, pbc=True)
stid, bl = min(enumerate(bls), key=itemgetter(1))
st = hsl[stid]
if bl > self.dmax:
continue
adsids = next((l for l in ads_list if adsid in l), None)
adsi = tuple(sorted(adsids))
if 'occupied' in st:
if bl >= st['bond_length']:
continue
elif self.symbols[adsid] != 'H':
if adsi in ndentate_dict:
ndentate_dict[adsi] -= 1
st['bonding_index'] = adsid
st['bond_length'] = bl
symbols = ''.join(list(Formula(str(self.symbols[adsids]))))
adssym = next((k for k, v in adsorbate_formulas.items()
if v == symbols), None)
if adssym is None:
adssym = next((k for k, v in adsorbate_formulas.items()
if sorted(v) == sorted(symbols)), symbols)
st['adsorbate'] = adssym
st['fragment'] = adssym
st['adsorbate_indices'] = adsi
if adsi in ndentate_dict:
ndentate_dict[adsi] += 1
else:
ndentate_dict[adsi] = 1
st['occupied'] = 1
# Get dentate numbers and coverage
self.n_occupied, n_surf_occupied, n_subsurf_occupied = 0, 0, 0
for st in hsl:
if 'occupied' not in st:
st['bonding_index'] = st['bond_length'] = None
st['adsorbate'] = st['fragment'] = None
st['adsorbate_indices'] = None
st['occupied'] = st['dentate'] = 0
st['fragment_indices'] = None
st['label'] = 0
continue
self.n_occupied += 1
if st['morphology'] == 'subsurf':
n_subsurf_occupied += 1
else:
n_surf_occupied += 1
adsi = st['adsorbate_indices']
if adsi in ndentate_dict:
st['dentate'] = ndentate_dict[adsi]
else:
st['dentate'] = 0
self.coverage = n_surf_occupied / len(self.surf_ids)
self.subsurf_coverage = n_subsurf_occupied / len(self.subsurf_ids)
# Identify bidentate fragments and assign labels
self.multidentate_fragments = []
self.monodentate_adsorbate_list = []
self.multidentate_labels = []
multidentate_adsorbate_dict = {}
for j, st in enumerate(hsl):
if st['occupied']:
adssym = st['adsorbate']
if st['dentate'] > 1:
bondid = st['bonding_index']
bondsym = self.symbols[bondid]
fsym = next((f for f in string_fragmentation(adssym)
if f[0] == bondsym), None)
st['fragment'] = fsym
flen = len(list(Formula(fsym)))
adsids = st['adsorbate_indices']
ibond = adsids.index(bondid)
fsi = adsids[ibond:ibond+flen]
st['fragment_indices'] = fsi
if adsids not in multidentate_adsorbate_dict:
multidentate_adsorbate_dict[adsids] = adssym
else:
st['fragment_indices'] = st['adsorbate_indices']
self.monodentate_adsorbate_list.append(adssym)
if self.label_occupied_sites:
if len(self.metals) > 2:
stlab = 0
else:
if st['label'] is None:
signature = [st['site'], st['morphology']]
if self.composition_effect:
signature.append(st['composition'])
stlab = self.label_dict['|'.join(signature)]
else:
stlab = st['label']
label = str(stlab) + st['fragment']
st['label'] = label
ll[j] = label
if st['dentate'] > 1:
self.multidentate_fragments.append(label)
if bondid == adsids[0]:
mdlabel = str(stlab) + adssym
self.multidentate_labels.append(mdlabel)
self.multidentate_adsorbate_list = list(multidentate_adsorbate_dict.values())
self.adsorbate_list = self.monodentate_adsorbate_list + \
self.multidentate_adsorbate_list
[docs] def get_site(self, indices):
"""Get information of a site given its atom indices.
Parameters
----------
indices : list or tuple
The indices of the atoms that contribute to the site.
"""
indices = indices if is_list_or_tuple(indices) else [indices]
indices = tuple(sorted(indices))
st = next((s for s in self.hetero_site_list if
s['indices'] == indices), None)
return st
[docs] def get_sites(self, occupied_only=False):
"""Get information of all sites.
Parameters
----------
occupied_only : bool, default False
Whether to only return occupied sites.
"""
all_sites = self.hetero_site_list
if occupied_only:
all_sites = [s for s in all_sites if s['occupied']]
return all_sites
[docs] def get_adsorbates(self):
"""Get a list of tuples that contains each adsorbate (string)
and the corresponding adsorbate indices (tuple)."""
adsorbates = []
adsorbate_ids = set()
for st in self.hetero_site_list:
if st['occupied']:
adsi = st['adsorbate_indices']
if not set(adsi).issubset(adsorbate_ids):
adsorbates.append((st['adsorbate'], adsi))
adsorbate_ids.update(adsi)
return adsorbates
def make_ads_neighbor_list(self, dx=.2, neighbor_number=1):
self.ads_nblist = neighbor_shell_list(self.ads_atoms, dx,
neighbor_number, mic=True)
[docs] def get_occupied_labels(self, fragmentation=True):
"""Get a list of labels of all occupied sites. The label consists
of a numerical part that represents site, and a character part
that represents the occupying adsorbate.
Parameters
----------
fragmentation : bool, default True
Whether to cut multidentate species into fragments. This ensures
that multidentate species with different orientations have
different labels.
"""
if not self.label_occupied_sites:
return self.atoms[self.ads_ids].get_chemical_formula(mode='hill')
ll = self.label_list
labs = [lab for lab in ll if lab != '0']
if not fragmentation:
mf = self.multidentate_fragments
mdlabs = self.multidentate_labels
c1, c2 = Counter(labs), Counter(mf)
diff = list((c1 - c2).elements())
labs = diff + mdlabs
return sorted(labs)
[docs] def get_graph(self, fragmentation=True, subsurf_effect=False):
"""Get the graph representation of the nanoparticle with adsorbates.
Parameters
----------
fragmentation : bool, default True
Whether to cut multidentate species into fragments. This ensures
that multidentate species with different orientations have
different graphs.
subsurf_effect : bool, default False
Take subsurface atoms into consideration when genearting graph.
"""
hsl = self.hetero_site_list
hcm = self.connectivity_matrix.copy()
if subsurf_effect:
surf_ids = self.surf_ids + self.subsurf_ids
else:
surf_ids = self.surf_ids
surfhcm = hcm[surf_ids]
symbols = self.symbols[surf_ids]
nrows, ncols = surfhcm.shape
newrows, frag_list = [], []
for st in hsl:
if st['occupied']:
if not fragmentation and st['dentate'] > 1:
if st['bonding_index'] != st['adsorbate_indices'][0]:
continue
si = st['indices']
newrow = np.zeros(ncols)
newrow[list(si)] = 1
newrows.append(newrow)
if fragmentation:
frag_list.append(st['fragment'])
else:
frag_list.append(st['adsorbate'])
if newrows:
surfhcm = np.vstack((surfhcm, np.asarray(newrows)))
G = nx.Graph()
# Add nodes from fragment list
G.add_nodes_from([(i, {'symbol': symbols[i]})
for i in range(nrows)] +
[(j + nrows, {'symbol': frag_list[j]})
for j in range(len(frag_list))])
# Add edges from surface connectivity matrix
shcm = surfhcm[:,surf_ids]
shcm = shcm * np.tri(*shcm.shape, k=-1)
rows, cols = np.where(shcm == 1)
edges = zip(rows.tolist(), cols.tolist())
G.add_edges_from(edges)
return G
# def get_surface_bond_count_matrix(self, species):
# hsl = self.hetero_site_list
# cm = self.connectivity_matrix
# atoms = self.atoms
# numbers = atoms.numbers
# symbols = atoms.symbols
# specs = species
# specs.sort(key=lambda x: atomic_numbers[x])
# ncols = len(specs) + 1
# sbcm = np.zeros((len(atoms), ncols))
# for st in hsl:
# frags = list(Formula(st['fragment']))
# counts = Counter(frags)
# for i in st['indices']:
# for j, spec in enumerate(specs):
# sbcm[i,j] += counts[spec]
# top_ids = self.surf_ids + self.subsurf_ids if \
# self.allow_6fold else self.surf_ids
# for si in top_ids:
# nbids = np.where(cm[si]==1)[0]
# nbs = [symbols[i] for i in nbids]
# nbcounts = Counter(nbs)
# for j, spec in enumerate(specs):
# sbcm[si,j] += nbcounts[spec]
# sbcm[si,ncols-1] = numbers[si]
#
# return sbcm[top_ids]
[docs] def get_coverage(self):
"""Get the adsorbate coverage (ML) of the surface."""
return self.coverage
[docs] def get_subsurf_coverage(self):
"""Get the adsorbate coverage (ML) of the subsurface."""
return self.subsurf_coverage
[docs]def enumerate_occupied_sites(atoms, adsorption_sites=None,
surface=None,
both_sides=False,
label_occupied_sites=False,
dmax=2.5):
"""A function that enumerates all occupied adsorption sites of
the input atoms object. The function is generalized for both
periodic and non-periodic systems (distinguished by atoms.pbc).
Parameters
----------
atoms : ase.Atoms object
Accept any ase.Atoms object. No need to be built-in.
adsorption_sites : acat.adsorption_sites.ClusterAdsorptionSites \
object or acat.adsorption_sites.SlabAdsorptionSites object, \
default None
The built-in adsorption sites class.
surface : str, default None
The surface type (crystal structure + Miller indices)
If the structure is a periodic surface slab, this is required.
If the structure is a nanoparticle, the function enumerates
only the sites on the specified surface.
both_sides : bool, default False
Whether to consider adsorbate coverage on both top and bottom
sides of the slab. Only works if adsorption_sites is not provided,
otherwise consistent with what is specified in adsorption_sites.
label_occupied_sites : bool, default False
Whether to assign a label to the occupied each site. The string
of the occupying adsorbate is concatentated to the numerical
label that represents the occpied site. Since the site labelling
currently only supports monometallics and bimetallics, the
numerical part will all be 0 if there are more than 2 metal
components.
dmax : float, default 2.5
The maximum bond length (in Angstrom) between the site and the
bonding atom that should be considered as an adsorbate.
Example
-------
This is an example of enumerating all occupied sites on a truncated
octahedral nanoparticle:
>>> from acat.adsorption_sites import ClusterAdsorptionSites
>>> from acat.adsorbate_coverage import enumerate_occupied_sites
>>> from acat.build.actions import add_adsorbate_to_site
>>> from ase.cluster import Octahedron
>>> atoms = Octahedron('Ni', length=7, cutoff=2)
>>> for atom in atoms:
... if atom.index % 2 == 0:
... atom.symbol = 'Pt'
>>> atoms.center(vacuum=5.)
>>> cas = ClusterAdsorptionSites(atoms, composition_effect=True)
>>> sites = cas.get_sites()
>>> for s in sites:
... if s['site'] == 'ontop':
... add_adsorbate_to_site(atoms, adsorbate='OH', site=s)
>>> sites = enumerate_occupied_sites(atoms, adsorption_sites=cas)
>>> print(sites[0])
Output:
.. code-block:: python
{'site': 'ontop', 'surface': 'fcc111',
'position': array([ 6.76, 8.52, 10.28]),
'normal': array([-0.57735027, -0.57735027, -0.57735027]),
'indices': (2,), 'composition': 'Pt', 'subsurf_index': None,
'subsurf_element': None, 'label': None, 'bonding_index': 201,
'bond_length': 1.7999999999999996, 'adsorbate': 'OH',
'fragment': 'OH', 'adsorbate_indices': (201, 202),
'occupied': 1, 'dentate': 1, 'fragment_indices': (201, 202)}
"""
if True not in atoms.pbc:
cac = ClusterAdsorbateCoverage(atoms, adsorption_sites, surface,
label_occupied_sites, dmax)
all_sites = cac.hetero_site_list
if surface:
occupied_sites = [s for s in all_sites if s['surface']
== surface and s['occupied']]
else:
occupied_sites = [s for s in all_sites if s['occupied']]
else:
sac = SlabAdsorbateCoverage(atoms, adsorption_sites, surface,
both_sides, label_occupied_sites, dmax)
all_sites = sac.hetero_site_list
occupied_sites = [s for s in all_sites if s['occupied']]
return occupied_sites