def ads_av(self, atoms=None):
"""Function that takes an atoms objects and returns a fingerprint
vector with averages of the atomic properties of the adsorbate.
Parameters
----------
atoms : object
ASE Atoms object.
Returns
----------
features : list
If None was passed, the elements are strings, naming the feature.
"""
ads_params = default_params + ['econf', 'ionenergies']
labels = make_labels(ads_params, '', '_ads_av')
labels.append('ground_state_magmom_ads_av')
if atoms is None:
return labels
else:
numbers = [atoms[j].number for j in atoms.subsets['ads_atoms']]
dat = list_mendeleev_params(numbers, params=ads_params)
result = list(np.nanmean(dat, axis=0))
result += [np.nanmean([gs_magmom[z] for z in numbers])]
check_labels(labels, result, atoms)
return result
def term(self, atoms=None):
"""Return a fingerprint vector with propeties averaged over the
termination atoms.
Parameters
----------
atoms : object
"""
labels = make_labels(self.slab_params, '', '_term')
labels.append('ground_state_magmom_term')
if atoms is None:
return labels
else:
if ('key_value_pairs' in atoms.info and
'term' in atoms.info['key_value_pairs']):
term = atoms.info['key_value_pairs']['term']
numbers = [atomic_numbers[s] for s in string2symbols(term)]
elif 'termination_atoms' in atoms.subsets:
term = atoms.subsets['termination_atoms']
numbers = atoms.numbers[term]
else:
raise NotImplementedError("termination fingerprint.")
dat = list_mendeleev_params(numbers, params=self.slab_params)
result = list(np.nanmean(dat, axis=0))
result += [np.nanmean([gs_magmom[z] for z in numbers])]
check_labels(labels, result, atoms)
return result
def mean_surf_ligands(self, atoms=None):
"""Function that takes an atoms objects and returns a fingerprint
vector containing the count of nearest neighbors and properties of
the nearest neighbors.
Parameters
----------
atoms : object
ASE Atoms object.
Returns
----------
features : list
If None was passed, the elements are strings, naming the feature.
"""
labels = ['nn_surf_ligands', 'identnn_surf_ligands']
labels += make_labels(self.slab_params, '', '_surf_ligands')
labels.append('ground_state_magmom_surf_ligands')
if atoms is None:
return labels
else:
ligand_atoms = atoms.subsets['ligand_atoms']
numbers = atoms.numbers[ligand_atoms]
# Import CatLearn data on that element.
dat = list_mendeleev_params(numbers, params=self.slab_params)
result = list(np.nanmean(dat, axis=0))
result += [np.nanmean([gs_magmom[z] for z in numbers])]
# Append count of ligand atoms.
result = [len(ligand_atoms), len(np.unique(numbers))] + result
check_labels(labels, result, atoms)
return result
def sum_site(self, atoms=None):
"""Function that takes an atoms objects and returns a fingerprint
vector with properties summed over the surface metal atoms
closest to an add atom.
Parameters
----------
atoms : object
ASE Atoms object.
Returns
----------
features : list
If None was passed, the elements are strings, naming the feature.
"""
labels = make_labels(self.slab_params, '', '_site_sum')
labels.append('ground_state_magmom_site_sum')
if atoms is None:
return labels
else:
numbers = [atoms[j].number for j in atoms.subsets['site_atoms']]
dat = list_mendeleev_params(numbers, params=self.slab_params)
result = list(np.nansum(dat, axis=0))
result += [np.nansum([gs_magmom[z] for z in numbers])]
check_labels(labels, result, atoms)
return result
def mean_chemisorbed_atoms(self, atoms=None):
"""Function that takes an atoms objects and returns a fingerprint
vector containing properties of the closest add atom to a surface
metal atom.
Parameters
----------
atoms : object
ASE Atoms object.
Returns
----------
features : list
If None was passed, the elements are strings, naming the feature.
"""
extra_ads_params = ['atomic_radius', 'heat_of_formation',
'oxistates', 'block', 'econf', 'ionenergies']
labels = make_labels(default_params + extra_ads_params, '', '_ads1')
labels.append('ground_state_magmom_ads1')
if atoms is None:
return labels
else:
# Get atomic number of alpha adsorbate atom.
chemisorbed_atoms = atoms.subsets['chemisorbed_atoms']
numbers = atoms.numbers[chemisorbed_atoms]
# Import CatLearn data on that element.
dat = list_mendeleev_params(numbers, params=default_params +
extra_ads_params)
result = list(np.nanmean(dat, axis=0))
result += [np.nanmean([gs_magmom[z] for z in numbers])]
check_labels(labels, result, atoms)
return result
def bulk(self, atoms=None):
"""Return a fingerprint vector with propeties averaged over
the bulk atoms.
Parameters
----------
atoms : object
ASE Atoms object.
Returns
----------
features : list
If None was passed, the elements are strings, naming the feature.
"""
labels = make_labels(self.slab_params, '', '_bulk')
labels.append('ground_state_magmom_bulk')
if atoms is None:
return labels
else:
if ('key_value_pairs' in atoms.info and
'bulk' in atoms.info['key_value_pairs']):
bulk = atoms.info['key_value_pairs']['bulk']
numbers = [atomic_numbers[s] for s in string2symbols(bulk)]
elif 'bulk_atoms' in atoms.subsets:
bulk = atoms.subsets['bulk_atoms']
numbers = atoms.numbers[bulk]
else:
raise NotImplementedError("bulk fingerprint.")
dat = list_mendeleev_params(numbers, params=self.slab_params)
result = list(np.nanmean(dat, axis=0))
result += [np.nanmean([gs_magmom[z] for z in numbers])]
check_labels(labels, result, atoms)
return result
def median_site(self, atoms=None):
"""Function that takes an atoms objects and returns a fingerprint
vector with properties averaged over the surface metal atoms
closest to an add atom.
Parameters
----------
atoms : object
"""
labels = make_labels(self.slab_params, '', '_site_med')
labels.append('ground_state_magmom_site_med')
if atoms is None:
return labels
else:
numbers = [atoms[j].number for j in atoms.subsets['site_atoms']]
dat = list_mendeleev_params(numbers, params=self.slab_params)
result = list(np.nanmedian(dat, axis=0))
result += [np.nanmedian([gs_magmom[z] for z in numbers])]
check_labels(labels, result, atoms)
return result