def test_all_nn_classes(self):
self.assertEqual(MinimumDistanceNN(cutoff=5, get_all_sites=True).get_cn(
self.cscl, 0), 14)
self.assertEqual(MinimumDistanceNN().get_cn(self.diamond, 0), 4)
self.assertEqual(MinimumDistanceNN().get_cn(self.nacl, 0), 6)
self.assertEqual(MinimumDistanceNN().get_cn(self.lifepo4, 0), 6)
self.assertEqual(MinimumDistanceNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertEqual(MinimumDistanceNN(tol=0.1).get_cn(self.mos2, 0), 6)
for image in MinimumDistanceNN(tol=0.1).get_nn_images(self.mos2, 0):
self.assertTrue(image in [(0, 0, 0), (0, 1, 0), (-1, 0, 0),
(0, 0, 0), (0, 1, 0), (-1, 0, 0)])
okeeffe = MinimumOKeeffeNN(tol=0.01)
self.assertEqual(okeeffe.get_cn(self.diamond, 0), 4)
self.assertEqual(okeeffe.get_cn(self.nacl, 0), 6)
self.assertEqual(okeeffe.get_cn(self.cscl, 0), 8)
self.assertEqual(okeeffe.get_cn(self.lifepo4, 0), 2)
virenn = MinimumVIRENN(tol=0.01)
self.assertEqual(virenn.get_cn(self.diamond, 0), 4)
self.assertEqual(virenn.get_cn(self.nacl, 0), 6)
self.assertEqual(virenn.get_cn(self.cscl, 0), 8)
self.assertEqual(virenn.get_cn(self.lifepo4, 0), 2)
brunner_recip = BrunnerNN_reciprocal(tol=0.01)
self.assertEqual(brunner_recip.get_cn(self.diamond, 0), 4)
self.assertEqual(brunner_recip.get_cn(self.nacl, 0), 6)
self.assertEqual(brunner_recip.get_cn(self.cscl, 0), 14)
self.assertEqual(brunner_recip.get_cn(self.lifepo4, 0), 6)
brunner_rel = BrunnerNN_relative(tol=0.01)
self.assertEqual(brunner_rel.get_cn(self.diamond, 0), 4)
self.assertEqual(brunner_rel.get_cn(self.nacl, 0), 6)
self.assertEqual(brunner_rel.get_cn(self.cscl, 0), 14)
self.assertEqual(brunner_rel.get_cn(self.lifepo4, 0), 6)
brunner_real = BrunnerNN_real(tol=0.01)
self.assertEqual(brunner_real.get_cn(self.diamond, 0), 4)
self.assertEqual(brunner_real.get_cn(self.nacl, 0), 6)
self.assertEqual(brunner_real.get_cn(self.cscl, 0), 14)
self.assertEqual(brunner_real.get_cn(self.lifepo4, 0), 30)
econn = EconNN()
self.assertEqual(econn.get_cn(self.diamond, 0), 4)
self.assertEqual(econn.get_cn(self.nacl, 0), 6)
self.assertEqual(econn.get_cn(self.cscl, 0), 14)
self.assertEqual(econn.get_cn(self.lifepo4, 0), 6)
voroinn = VoronoiNN(tol=0.5)
self.assertEqual(voroinn.get_cn(self.diamond, 0), 4)
self.assertEqual(voroinn.get_cn(self.nacl, 0), 6)
self.assertEqual(voroinn.get_cn(self.cscl, 0), 8)
self.assertEqual(voroinn.get_cn(self.lifepo4, 0), 6)
crystalnn = CrystalNN()
self.assertEqual(crystalnn.get_cn(self.diamond, 0), 4)
self.assertEqual(crystalnn.get_cn(self.nacl, 0), 6)
self.assertEqual(crystalnn.get_cn(self.cscl, 0), 8)
self.assertEqual(crystalnn.get_cn(self.lifepo4, 0), 6)
def test_all_nn_classes(self):
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.1).get_cn(self.mos2, 0), 6)
for image in MinimumDistanceNN(tol=0.1).get_nn_images(self.mos2, 0):
self.assertTrue(image in [(0, 0, 0), (0, 1,
0), (-1, 0,
0), (0, 0,
0), (0, 1,
0), (-1, 0,
0)])
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumVIRENN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
BrunnerNN_reciprocal(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(
BrunnerNN_reciprocal(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
BrunnerNN_reciprocal(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(
BrunnerNN_relative(tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN_relative(tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN_relative(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
BrunnerNN_real(tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN_real(tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN_real(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(VoronoiNN(tol=0.5).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(VoronoiNN(tol=0.5).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(VoronoiNN(tol=0.5).get_cn(self.cscl, 0), 8)
def test_all_nn_classes(self):
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.1).get_cn(self.mos2, 0), 6)
for image in MinimumDistanceNN(tol=0.1).get_nn_images(self.mos2, 0):
self.assertTrue(image in [[0, 0, 0], [0, 1, 0], [-1, 0, 0], \
[0, 0, 0], [0, 1, 0], [-1, 0, 0]])
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumVIRENN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(
BrunnerNN(mode="real", tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN(mode="real", tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN(mode="real", tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
BrunnerNN(mode="relative", tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN(mode="relative", tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN(mode="relative", tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(VoronoiNN_modified().get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(VoronoiNN_modified().get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(VoronoiNN_modified().get_cn(self.cscl, 0), 8)
def fragment_to_molgraph(fragment: Fragment) -> MoleculeGraph:
"""
[summary]
Parameters
----------
fragment : Fragment
[description]
Returns
-------
pymatgen.analysis.graphs.MoleculeGraph
[description]
"""
mol = fragment.atoms.copy()
dummies_idx = fragment.atoms.indices_from_symbol("X")
mol.replace_species({"X": "H"})
# setting up UFF type analysis
uff_lib, uff_symbs = load_uff_lib(mol)
# obtaining cutoffs from the maximum UFF radius
strategy = EconNN(tol=0.3, use_fictive_radius=True, cutoff=10.0)
mg = MoleculeGraph.with_local_env_strategy(mol, strategy=strategy)
# add mmtypes
mmtypes = find_mmtypes(molgraph=mg, uff_lib=uff_lib, uff_symbs=uff_symbs)
for i, mmtype in enumerate(mmtypes):
mg.molecule[i].properties["ufftype"] = mmtype
# transfer tags from dummies
if "tags" in mg.molecule.site_properties:
for dummy_idx in dummies_idx:
tag = mg.molecule[dummy_idx].properties["tags"]
for site in mg.get_connected_sites(dummy_idx):
mg.molecule[site.index].properties["tags"] = tag
# remove dummies
mg.remove_nodes(list(dummies_idx))
return mg
def get_local_env_method(method): # pylint:disable=too-many-return-statements
"""get a local environment method based on its name"""
method = method.lower()
if method.lower() == "crystalnn":
# see eq. 15 and 16 in
# https://pubs.acs.org/doi/full/10.1021/acs.inorgchem.0c02996
# for the x_diff_weight parameter.
# in the paper it is called δen and it is set to 3
# we found better results by lowering this weight
return CrystalNN(porous_adjustment=True,
x_diff_weight=1.5,
search_cutoff=4.5)
if method.lower() == "econnn":
return EconNN()
if method.lower() == "brunnernn":
return BrunnerNN_relative()
if method.lower() == "minimumdistance":
return MinimumDistanceNN()
if method.lower() == "vesta":
return VESTA_NN
if method.lower() == "voronoinn":
return VoronoiNN()
if method.lower() == "atr":
return ATR_NN
if method.lower() == "li":
return LI_NN
return JmolNN()
def setUp(self):
# set up a test structure, the coordinations are not correct and are only
# for test purposes.
structure = Structure.from_spacegroup(
225,
[[5.7, 0, 0], [0, 5.7, 0], [0, 0, 5.7]],
["Na1+", "Cl1-"],
[[0, 0, 0], [0.5, 0, 0]],
)
all_sites_coordination = [{"Cl": 6}] * 4 + [{"Na": 8, "Cl": [6, 8]}] * 4
structure.add_site_property("coordination", all_sites_coordination)
self.structures = {"test_structure": structure}
self.nn_methods = [MinimumVIRENN(), EconNN()]
def get_NNs_pm(atoms, site_idx, NN_method):
"""
Get coordinating atoms to the adsorption site
Args:
atoms (Atoms object): atoms object of MOF
site_idx (int): ASE index of adsorption site
NN_method (string): string representing the desired Pymatgen
nearest neighbor algorithm:
refer to http://pymatgen.org/_modules/pymatgen/analysis/local_env.html
Returns:
neighbors_idx (list of ints): ASE indices of coordinating atoms
"""
#Convert ASE Atoms object to Pymatgen Structure object
bridge = pm_ase.AseAtomsAdaptor()
struct = bridge.get_structure(atoms)
#Select Pymatgen NN algorithm
NN_method = NN_method.lower()
if NN_method == 'vire':
nn_object = MinimumVIRENN()
elif NN_method == 'voronoi':
nn_object = VoronoiNN()
elif NN_method == 'jmol':
nn_object = JmolNN()
elif NN_method == 'min_dist':
nn_object = MinimumDistanceNN()
elif NN_method == 'okeeffe':
nn_object = MinimumOKeeffeNN()
elif NN_method == 'brunner_real':
nn_object = BrunnerNN_real()
elif NN_method == 'brunner_recpirocal':
nn_object = BrunnerNN_reciprocal()
elif NN_method == 'brunner_relative':
nn_object = BrunnerNN_relative()
elif NN_method == 'econ':
nn_object = EconNN()
elif NN_method == 'dict':
#requires a cutoff dictionary located in the pwd
nn_object = CutOffDictNN(cut_off_dict='cut_off_dict.txt')
elif NN_method == 'critic2':
nn_object = Critic2NN()
elif NN_method == 'openbabel':
nn_object = OpenBabelNN()
elif NN_method == 'covalent':
nn_object = CovalentBondNN()
elif NN_method == 'crystal':
nn_object = CrystalNN(porous_adjustment=True)
elif NN_method == 'crystal_nonporous':
nn_object = CrystalNN(porous_adjustment=False)
else:
raise ValueError('Invalid NN algorithm specified')
#Find coordinating atoms
with warnings.catch_warnings():
warnings.simplefilter('ignore')
neighbors = nn_object.get_nn_info(struct, site_idx)
neighbors_idx = []
for neighbor in neighbors:
neighbors_idx.append(neighbor['site_index'])
return neighbors_idx