def test_gas_phase_example(self):
"""Test the gas phase enumerator examples."""
db_name = 'C2H6-example.db'
with ReactionNetwork(db_name=db_name) as rn:
rn.molecule_search(
element_pool={
'C': 2,
'H': 6
}, multiple_bond_search=False)
molecules = rn.load_molecules()
assert (len(molecules) == 17)
rn.path_search(reconfiguration=False, substitution=False)
pathways = rn.load_pathways()
assert (len(pathways) == 27)
names = np.empty(len(molecules) + 1, dtype='a5')
names[0] = ''
for k, v in molecules.items():
atn = nx.get_node_attributes(v.graph, 'number')
hill = formula_hill(list(atn.values()))
names[k] = hill
for path in pathways:
print('|{} + {} --> {} + {}|'.format(*names[path]))
def _get_species_name(self, index):
site = self._get_site_index(index)
if self.formula == 'hill':
n = nx.get_node_attributes(self.molecules[index], 'atomic_number')
name = formula_hill(list(n.values()))
elif self.formula == 'smiles':
name = get_smiles(self.molecules[index])
else:
raise NotImplementedError(str(self.formula))
return '_'.join([name, site])
def test_gas_phase_example():
db_name = 'C2H6-example.db'
with ReactionNetwork(db_name=db_name) as rn:
rn.molecule_search(element_pool={
'C': 2,
'H': 6
},
multiple_bond_search=False)
molecules = rn.load_molecules()
assert (len(molecules) == 17)
for i, molecule in molecules.items():
plot_molecule(molecule,
file_name='./images/molecule-{}.png'.format(i))
molecule = get_uff_coordinates(molecule, steps=50)
rn.save_3d_structure(molecule)
images = rn.load_3d_structures()
assert (len(images) == 17)
rn.path_search(reconfiguration=False, substitution=False)
rn.plot_reaction_network(file_name='./images/reaction-network.png')
pathways = rn.load_pathways()
assert (len(pathways) == 27)
names = np.empty(len(molecules) + 1, dtype='a5')
names[0] = ''
for k, v in molecules.items():
atn = nx.get_node_attributes(v.graph, 'number')
hill = formula_hill(list(atn.values()))
names[k] = hill
for path in pathways:
print('|{} + {} --> {} + {}|'.format(*names[path]))
os.unlink(db_name)
def get_chemical_formula(self, mode='hill', empirical=False):
"""Get chemical formula.
See documentation of ase.atoms.Atoms.get_chemical_formula()."""
if mode in ('reduce', 'all') and empirical:
warnings.warn("Empirical chemical formula not available "
"for mode '{}'".format(mode))
if len(self) == 0:
return ''
numbers = self.numbers
if mode == 'reduce':
n = len(numbers)
changes = np.concatenate(
([0], np.arange(1, n)[numbers[1:] != numbers[:-1]]))
symbols = [chemical_symbols[e] for e in numbers[changes]]
counts = np.append(changes[1:], n) - changes
tokens = []
for s, c in zip(symbols, counts):
tokens.append(s)
if c > 1:
tokens.append(str(c))
formula = ''.join(tokens)
elif mode == 'hill':
formula = formula_hill(numbers, empirical=empirical)
elif mode == 'all':
formula = ''.join([chemical_symbols[n] for n in numbers])
elif mode == 'metal':
formula = formula_metal(numbers, empirical=empirical)
else:
raise ValueError("Use mode = 'all', 'reduce', 'hill' or 'metal'.")
return formula
def __init__(self, references, filter='', verbose=True):
"""Phase-diagram.
references: list of (name, energy) tuples
List of references. The energy must be the total energy and not
energy per atom. The names can also be dicts like
``{'Zn': 1, 'O': 2}`` which would be equivalent to ``'ZnO2'``.
filter: str or list of str
Use only those references that match the given filter.
Example: ``filter='ZnO'`` will select those that
contain zinc or oxygen.
verbose: bool
Write information.
"""
if not references:
raise ValueError("You must provide a non-empty list of references"
" for the phase diagram! "
"You have provided '{}'".format(references))
filter = parse_formula(filter)[0]
self.verbose = verbose
self.species = OrderedDict()
self.references = []
for name, energy in references:
if isinstance(name, basestring):
count = parse_formula(name)[0]
else:
count = name
if filter and any(symbol not in filter for symbol in count):
continue
if not isinstance(name, basestring):
name = formula_hill(count)
natoms = 0
for symbol, n in count.items():
natoms += n
if symbol not in self.species:
self.species[symbol] = len(self.species)
self.references.append((count, energy, name, natoms))
ns = len(self.species)
self.symbols = [None] * ns
for symbol, id in self.species.items():
self.symbols[id] = symbol
if verbose:
print('Species:', ', '.join(self.symbols))
print('References:', len(self.references))
for i, (count, energy, name, natoms) in enumerate(self.references):
print('{:<5}{:10}{:10.3f}'.format(i, name, energy))
self.points = np.zeros((len(self.references), ns + 1))
for s, (count, energy, name, natoms) in enumerate(self.references):
for symbol, n in count.items():
self.points[s, self.species[symbol]] = n / natoms
self.points[s, -1] = energy / natoms
if len(self.points) == ns:
# Simple case that qhull would choke on:
self.simplices = np.arange(ns).reshape((1, ns))
self.hull = np.ones(ns, bool)
else:
hull = ConvexHull(self.points[:, 1:])
# Find relevant simplices:
ok = hull.equations[:, -2] < 0
self.simplices = hull.simplices[ok]
# Create a mask for those points that are on the convex hull:
self.hull = np.zeros(len(self.points), bool)
for simplex in self.simplices:
self.hull[simplex] = True
if verbose:
print('Simplices:', len(self.simplices))
def __init__(self, references, filter='', verbose=True):
"""Phase-diagram.
references: list of (name, energy) tuples
List of references. The energy must be the total energy and not
energy per atom. The names can also be dicts like
``{'Zn': 1, 'O': 2}`` which would be equivalent to ``'ZnO2'``.
filter: str or list of str
Use only those references that match the given filter.
Example: ``filter='ZnO'`` will select those that
contain zinc or oxygen.
verbose: bool
Write information.
"""
filter = parse_formula(filter)[0]
self.verbose = verbose
self.species = {}
self.references = []
for name, energy in references:
if isinstance(name, basestring):
count = parse_formula(name)[0]
else:
count = name
name = formula_hill(count)
if filter and any(symbol not in filter for symbol in count):
continue
natoms = 0
for symbol, n in count.items():
natoms += n
if symbol not in self.species:
self.species[symbol] = len(self.species)
self.references.append((count, energy, name, natoms))
self.symbols = [None] * len(self.species)
for symbol, id in self.species.items():
self.symbols[id] = symbol
if verbose:
print('Species:', ', '.join(self.symbols))
print('References:', len(self.references))
for i, (count, energy, name, natoms) in enumerate(self.references):
print('{0:<5}{1:10}{2:10.3f}'.format(i, name, energy))
self.points = np.zeros((len(self.references), len(self.species) + 1))
for s, (count, energy, name, natoms) in enumerate(self.references):
for symbol, n in count.items():
self.points[s, self.species[symbol]] = n / natoms
self.points[s, -1] = energy / natoms
hull = ConvexHull(self.points[:, 1:])
# Find relevant simplices:
ok = hull.equations[:, -2] < 0
self.simplices = hull.simplices[ok]
# Create a mask for those points that are on the convex hull:
self.hull = np.zeros(len(self.points), bool)
for simplex in self.simplices:
self.hull[simplex] = True
if verbose:
print('Simplices:', len(self.simplices))
def __init__(self, references, filter='', verbose=True):
"""Phase-diagram.
references: list of (name, energy) tuples
List of references. The energy must be the total energy and not
energy per atom. The names can also be dicts like
``{'Zn': 1, 'O': 2}`` which would be equivalent to ``'ZnO2'``.
filter: str or list of str
Use only those references that match the given filter.
Example: ``filter='ZnO'`` will select those that
contain zinc or oxygen.
verbose: bool
Write information.
"""
filter = parse_formula(filter)[0]
self.verbose = verbose
self.species = {}
self.references = []
for name, energy in references:
if isinstance(name, basestring):
count = parse_formula(name)[0]
else:
count = name
name = formula_hill(count)
if filter and any(symbol not in filter for symbol in count):
continue
natoms = 0
for symbol, n in count.items():
natoms += n
if symbol not in self.species:
self.species[symbol] = len(self.species)
self.references.append((count, energy, name, natoms))
self.symbols = [None] * len(self.species)
for symbol, id in self.species.items():
self.symbols[id] = symbol
if verbose:
print('Species:', ', '.join(self.symbols))
print('References:', len(self.references))
for i, (count, energy, name, natoms) in enumerate(self.references):
print('{0:<5}{1:10}{2:10.3f}'.format(i, name, energy))
self.points = np.zeros((len(self.references), len(self.species) + 1))
for s, (count, energy, name, natoms) in enumerate(self.references):
for symbol, n in count.items():
self.points[s, self.species[symbol]] = n / natoms
self.points[s, -1] = energy / natoms
hull = ConvexHull(self.points[:, 1:])
# Find relevant simplices:
ok = hull.equations[:, -2] < 0
self.simplices = hull.simplices[ok]
# Create a mask for those points that are on the convex hull:
self.hull = np.zeros(len(self.points), bool)
for simplex in self.simplices:
self.hull[simplex] = True
if verbose:
print('Simplices:', len(self.simplices))
