class JMolNNTest(PymatgenTest):
def setUp(self):
self.jmol = JMolNN()
self.jmol_update = JMolNN(el_radius_updates={"Li": 1})
def test_get_nn(self):
s = self.get_structure('LiFePO4')
# Test the default near-neighbor finder.
nsites_checked = 0
for site_idx, site in enumerate(s):
if site.specie == Element("Li"):
self.assertEqual(self.jmol.get_cn(s, site_idx), 0)
nsites_checked += 1
elif site.specie == Element("Fe"):
self.assertEqual(self.jmol.get_cn(s, site_idx), 6)
nsites_checked += 1
elif site.specie == Element("P"):
self.assertEqual(self.jmol.get_cn(s, site_idx), 4)
nsites_checked += 1
self.assertEqual(nsites_checked, 12)
# Test a user override that would cause Li to show up as 6-coordinated
self.assertEqual(self.jmol_update.get_cn(s, 0), 6)
# Verify get_nn function works
self.assertEqual(len(self.jmol_update.get_nn(s, 0)), 6)
def tearDown(self):
del self.jmol
del self.jmol_update
class JMolNNTest(PymatgenTest):
def setUp(self):
self.jmol = JMolNN()
self.jmol_update = JMolNN(el_radius_updates={"Li": 1})
def test_get_nn(self):
s = self.get_structure('LiFePO4')
# Test the default near-neighbor finder.
nsites_checked = 0
for site_idx, site in enumerate(s):
if site.specie == Element("Li"):
self.assertEqual(self.jmol.get_cn(s, site_idx), 0)
nsites_checked += 1
elif site.specie == Element("Fe"):
self.assertEqual(self.jmol.get_cn(s, site_idx), 6)
nsites_checked += 1
elif site.specie == Element("P"):
self.assertEqual(self.jmol.get_cn(s, site_idx), 4)
nsites_checked += 1
self.assertEqual(nsites_checked, 12)
# Test a user override that would cause Li to show up as 6-coordinated
self.assertEqual(self.jmol_update.get_cn(s, 0), 6)
# Verify get_nn function works
self.assertEqual(len(self.jmol_update.get_nn(s, 0)), 6)
def tearDown(self):
del self.jmol
del self.jmol_update
def test_cns(self):
cnv = CoordinationNumber.from_preset('VoronoiNN')
self.assertEqual(len(cnv.feature_labels()), 1)
self.assertEqual(cnv.feature_labels()[0], 'CN_VoronoiNN')
self.assertAlmostEqual(cnv.featurize(self.sc, 0)[0], 6)
self.assertAlmostEqual(cnv.featurize(self.cscl, 0)[0], 14)
self.assertAlmostEqual(cnv.featurize(self.cscl, 1)[0], 14)
self.assertEqual(len(cnv.citations()), 2)
cnv = CoordinationNumber(VoronoiNN(), use_weights='sum')
self.assertEqual(cnv.feature_labels()[0], 'CN_VoronoiNN')
self.assertAlmostEqual(cnv.featurize(self.cscl, 0)[0], 9.2584516)
self.assertAlmostEqual(cnv.featurize(self.cscl, 1)[0], 9.2584516)
self.assertEqual(len(cnv.citations()), 2)
cnv = CoordinationNumber(VoronoiNN(), use_weights='effective')
self.assertEqual(cnv.feature_labels()[0], 'CN_VoronoiNN')
self.assertAlmostEqual(cnv.featurize(self.cscl, 0)[0], 11.648923254)
self.assertAlmostEqual(cnv.featurize(self.cscl, 1)[0], 11.648923254)
self.assertEqual(len(cnv.citations()), 2)
cnj = CoordinationNumber.from_preset('JMolNN')
self.assertEqual(cnj.feature_labels()[0], 'CN_JMolNN')
self.assertAlmostEqual(cnj.featurize(self.sc, 0)[0], 0)
self.assertAlmostEqual(cnj.featurize(self.cscl, 0)[0], 0)
self.assertAlmostEqual(cnj.featurize(self.cscl, 1)[0], 0)
self.assertEqual(len(cnj.citations()), 1)
jmnn = JMolNN(el_radius_updates={"Al": 1.55, "Cl": 1.7, "Cs": 1.7})
cnj = CoordinationNumber(jmnn)
self.assertEqual(cnj.feature_labels()[0], 'CN_JMolNN')
self.assertAlmostEqual(cnj.featurize(self.sc, 0)[0], 6)
self.assertAlmostEqual(cnj.featurize(self.cscl, 0)[0], 8)
self.assertAlmostEqual(cnj.featurize(self.cscl, 1)[0], 8)
self.assertEqual(len(cnj.citations()), 1)
cnmd = CoordinationNumber.from_preset('MinimumDistanceNN')
self.assertEqual(cnmd.feature_labels()[0], 'CN_MinimumDistanceNN')
self.assertAlmostEqual(cnmd.featurize(self.sc, 0)[0], 6)
self.assertAlmostEqual(cnmd.featurize(self.cscl, 0)[0], 8)
self.assertAlmostEqual(cnmd.featurize(self.cscl, 1)[0], 8)
self.assertEqual(len(cnmd.citations()), 1)
cnmok = CoordinationNumber.from_preset('MinimumOKeeffeNN')
self.assertEqual(cnmok.feature_labels()[0], 'CN_MinimumOKeeffeNN')
self.assertAlmostEqual(cnmok.featurize(self.sc, 0)[0], 6)
self.assertAlmostEqual(cnmok.featurize(self.cscl, 0)[0], 8)
self.assertAlmostEqual(cnmok.featurize(self.cscl, 1)[0], 6)
self.assertEqual(len(cnmok.citations()), 2)
cnmvire = CoordinationNumber.from_preset('MinimumVIRENN')
self.assertEqual(cnmvire.feature_labels()[0], 'CN_MinimumVIRENN')
self.assertAlmostEqual(cnmvire.featurize(self.sc, 0)[0], 6)
self.assertAlmostEqual(cnmvire.featurize(self.cscl, 0)[0], 8)
self.assertAlmostEqual(cnmvire.featurize(self.cscl, 1)[0], 14)
self.assertEqual(len(cnmvire.citations()), 2)
self.assertEqual(len(cnmvire.implementors()), 2)
self.assertEqual(cnmvire.implementors()[0], 'Nils E. R. Zimmermann')
def get_max_bond_lengths(structure, el_radius_updates=None):
"""
Provides max bond length estimates for a structure based on the JMol
table and algorithms.
Args:
structure: (structure)
el_radius_updates: (dict) symbol->float to update atomic radii
Returns: (dict) - (Element1, Element2) -> float. The two elements are
ordered by Z.
"""
#jmc = JMolCoordFinder(el_radius_updates)
jmnn = JMolNN(el_radius_updates)
bonds_lens = {}
els = sorted(structure.composition.elements, key=lambda x: x.Z)
for i1 in range(len(els)):
for i2 in range(len(els) - i1):
bonds_lens[els[i1], els[i1 + i2]] = jmnn.get_max_bond_distance(
els[i1].symbol, els[i1 + i2].symbol)
return bonds_lens
def get_max_bond_lengths(structure, el_radius_updates=None):
"""
Provides max bond length estimates for a structure based on the JMol
table and algorithms.
Args:
structure: (structure)
el_radius_updates: (dict) symbol->float to update atomic radii
Returns: (dict) - (Element1, Element2) -> float. The two elements are
ordered by Z.
"""
#jmc = JMolCoordFinder(el_radius_updates)
jmnn = JMolNN(el_radius_updates)
bonds_lens = {}
els = sorted(structure.composition.elements, key=lambda x: x.Z)
for i1 in range(len(els)):
for i2 in range(len(els) - i1):
bonds_lens[els[i1], els[i1 + i2]] = jmnn.get_max_bond_distance(
els[i1].symbol, els[i1 + i2].symbol)
return bonds_lens
def find_connected_atoms(struct, tolerance=0.45, ldict=JMolNN().el_radius):
"""
Finds the list of bonded atoms.
Args:
struct (Structure): Input structure
tolerance: length in angstroms used in finding bonded atoms. Two atoms are considered bonded if (radius of atom 1) + (radius of atom 2) + (tolerance) < (distance between atoms 1 and 2). Default value = 0.45, the value used by JMol and Cheon et al.
ldict: dictionary of bond lengths used in finding bonded atoms. Values from JMol are used as default
standardize: works with conventional standard structures if True. It is recommended to keep this as True.
Returns:
connected_list: A numpy array of shape (number of bonded pairs, 2); each row of is of the form [atomi, atomj].
atomi and atomj are the indices of the atoms in the input structure.
If any image of atomj is bonded to atomi with periodic boundary conditions, [atomi, atomj] is included in the list.
If atomi is bonded to multiple images of atomj, it is only counted once.
"""
n_atoms = len(struct.species)
fc = np.array(struct.frac_coords)
species = list(map(str, struct.species))
#in case of charged species
for i, item in enumerate(species):
if not item in ldict.keys():
species[i] = str(Specie.from_string(item).element)
latmat = struct.lattice.matrix
connected_list = []
for i in range(n_atoms):
for j in range(i + 1, n_atoms):
max_bond_length = ldict[species[i]] + ldict[species[j]] + tolerance
add_ij = False
for move_cell in itertools.product([0, 1, -1], [0, 1, -1],
[0, 1, -1]):
if not add_ij:
frac_diff = fc[j] + move_cell - fc[i]
distance_ij = np.dot(latmat.T, frac_diff)
if np.linalg.norm(distance_ij) < max_bond_length:
add_ij = True
if add_ij:
connected_list.append([i, j])
return np.array(connected_list)
def setUp(self):
self.jmol = JMolNN()
self.jmol_update = JMolNN(el_radius_updates={"Li": 1})
def find_dimension(structure_raw,
tolerance=0.45,
ldict=JMolNN().el_radius,
standardize=True):
"""
Algorithm for finding the dimensions of connected subunits in a crystal structure.
This method finds the dimensionality of the material even when the material is not layered along low-index planes, or does not have flat layers/molecular wires.
See details at : Cheon, G.; Duerloo, K.-A. N.; Sendek, A. D.; Porter, C.; Chen, Y.; Reed, E. J. Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures. Nano Lett. 2017.
Args:
structure (Structure): Input structure
tolerance: length in angstroms used in finding bonded atoms. Two atoms are considered bonded if (radius of atom 1) + (radius of atom 2) + (tolerance) < (distance between atoms 1 and 2). Default value = 0.45, the value used by JMol and Cheon et al.
ldict: dictionary of bond lengths used in finding bonded atoms. Values from JMol are used as default
standardize: works with conventional standard structures if True. It is recommended to keep this as True.
Returns:
dim: dimension of the largest cluster as a string. If there are ions or molecules it returns 'intercalated ion/molecule'
"""
if standardize:
structure = SpacegroupAnalyzer(
structure_raw).get_conventional_standard_structure()
structure_save = copy.copy(structure_raw)
connected_list1 = find_connected_atoms(structure,
tolerance=tolerance,
ldict=ldict)
max1, min1, clusters1 = find_clusters(structure, connected_list1)
structure.make_supercell([[2, 0, 0], [0, 2, 0], [0, 0, 2]])
connected_list2 = find_connected_atoms(structure,
tolerance=tolerance,
ldict=ldict)
max2, min2, clusters2 = find_clusters(structure, connected_list2)
if min2 == 1:
dim = 'intercalated ion'
elif min2 == min1:
if max2 == max1:
dim = '0D'
else:
dim = 'intercalated molecule'
else:
dim = np.log2(float(max2) / max1)
if dim == int(dim):
dim = str(int(dim)) + 'D'
else:
structure = copy.copy(structure_save)
structure.make_supercell([[3, 0, 0], [0, 3, 0], [0, 0, 3]])
connected_list3 = find_connected_atoms(structure,
tolerance=tolerance,
ldict=ldict)
max3, min3, clusters3 = find_clusters(structure, connected_list3)
if min3 == min2:
if max3 == max2:
dim = '0D'
else:
dim = 'intercalated molecule'
else:
dim = np.log2(float(max3) / max1) / np.log2(3)
if dim == int(dim):
dim = str(int(dim)) + 'D'
else:
return
return dim
def setUp(self):
self.jmol = JMolNN()
self.jmol_update = JMolNN(el_radius_updates={"Li": 1})