def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError("SubstitutionPredictorTransformation doesn't"
" support returning 1 structure")
preds = self._substitutor.pred_from_comp(structure.composition)
preds.sort(key=lambda x: x['probability'], reverse=True)
outputs = []
for pred in preds:
st = SubstitutionTransformation(pred['substitutions'])
output = {
'structure': st.apply_transformation(structure),
'probability': pred['probability'],
'threshold': self._threshold,
'substitutions': {}
}
#dictionary keys have to be converted to strings for JSON
for key, value in pred['substitutions'].items():
output['substitutions'][str(key)] = str(value)
outputs.append(output)
return outputs
def test_append_transformation(self):
t = SubstitutionTransformation({"Fe": "Mn"})
self.trans.append_transformation(t)
self.assertEqual(
"NaMnPO4", self.trans.final_structure.composition.reduced_formula)
self.assertEqual(len(self.trans.structures), 3)
coords = list()
coords.append([0, 0, 0])
coords.append([0.75, 0.5, 0.75])
lattice = [[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603]]
struct = Structure(lattice, ["Si4+", "Si4+"], coords)
ts = TransformedStructure(struct, [])
ts.append_transformation(
SupercellTransformation.from_scaling_factors(2, 1, 1))
alt = ts.append_transformation(
PartialRemoveSpecieTransformation(
'Si4+',
0.5,
algo=PartialRemoveSpecieTransformation.ALGO_COMPLETE), 5)
self.assertEqual(len(alt), 2)
def test_fractional_substitution(self):
t = SubstitutionTransformation({
"Li+": "Na+",
"O2-": {
"S2-": 0.5,
"Se2-": 0.5
}
})
# test the to and from dict on the nested dictionary
t = SubstitutionTransformation.from_dict(t.as_dict())
coords = []
coords.append([0, 0, 0])
coords.append([0.75, 0.75, 0.75])
coords.append([0.5, 0.5, 0.5])
coords.append([0.25, 0.25, 0.25])
lattice = Lattice([
[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603],
])
struct = Structure(lattice, ["Li+", "Li+", "O2-", "O2-"], coords)
s = t.apply_transformation(struct)
self.assertEqual(s.composition.formula, "Na2 Se1 S1")
def pred_from_structures(self,
target_species,
structures_list,
remove_duplicates=True,
remove_existing=False):
"""
performs a structure prediction targeting compounds containing all of
the target_species, based on a list of structure (those structures
can for instance come from a database like the ICSD). It will return
all the structures formed by ionic substitutions with a probability
higher than the threshold
Notes:
If the default probability model is used, input structures must
be oxidation state decorated. See AutoOxiStateDecorationTransformation
This method does not change the number of species in a structure. i.e
if the number of target species is 3, only input structures containing
3 species will be considered.
Args:
target_species:
a list of species with oxidation states
e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)]
structures_list:
a list of dictionnary of the form {'structure':Structure object
,'id':some id where it comes from}
the id can for instance refer to an ICSD id.
remove_duplicates:
if True, the duplicates in the predicted structures will
be removed
remove_existing:
if True, the predicted structures that already exist in the
structures_list will be removed
Returns:
a list of TransformedStructure objects.
"""
target_species = get_el_sp(target_species)
result = []
transmuter = StandardTransmuter([])
if len(list(set(target_species) & set(self.get_allowed_species()))) \
!= len(target_species):
raise ValueError("the species in target_species are not allowed " +
"for the probability model you are using")
for permut in itertools.permutations(target_species):
for s in structures_list:
# check if: species are in the domain,
# and the probability of subst. is above the threshold
els = s['structure'].composition.elements
if len(els) == len(permut) and len(list(set(els) & set(self.get_allowed_species()))) == \
len(els) and self._sp.cond_prob_list(permut, els) > self._threshold:
clean_subst = {
els[i]: permut[i]
for i in range(0, len(els)) if els[i] != permut[i]
}
if len(clean_subst) == 0:
continue
transf = SubstitutionTransformation(clean_subst)
if Substitutor._is_charge_balanced(
transf.apply_transformation(s['structure'])):
ts = TransformedStructure(s['structure'], [transf],
history=[{
"source": s['id']
}],
other_parameters={
'type':
'structure_prediction',
'proba':
self._sp.cond_prob_list(
permut, els)
})
result.append(ts)
transmuter.append_transformed_structures([ts])
if remove_duplicates:
transmuter.apply_filter(
RemoveDuplicatesFilter(symprec=self._symprec))
if remove_existing:
# Make the list of structures from structures_list that corresponds to the
# target species
chemsys = list(set([sp.symbol for sp in target_species]))
structures_list_target = [
st['structure'] for st in structures_list
if Substitutor._is_from_chemical_system(
chemsys, st['structure'])
]
transmuter.apply_filter(
RemoveExistingFilter(structures_list_target,
symprec=self._symprec))
return transmuter.transformed_structures
def test_init(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
struct = self.get_structure("LiFePO4")
subtrans = SubstitutionTransformation({'Li': {'Li': 0.5}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1,
2)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 86)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")),
0.5 / 6.5)
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
2,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 52)
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
1,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 1)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")),
0.25 / 6.25)
# Make sure it works for completely disordered structures.
struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{
'Fe': 0.5
}], [[0, 0, 0]])
adaptor = EnumlibAdaptor(struct, 1, 2)
adaptor.run()
self.assertEqual(len(adaptor.structures), 3)
# Make sure it works properly when symmetry is broken by ordered sites.
struct = self.get_structure("LiFePO4")
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
s = subtrans.apply_transformation(struct)
# REmove some ordered sites to break symmetry.
removetrans = RemoveSitesTransformation([4, 7])
s = removetrans.apply_transformation(s)
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 4)
struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]], [{
"Si": 0.5
}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
adaptor = EnumlibAdaptor(struct,
1,
3,
enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 10)
struct = Structure.from_file(
os.path.join(test_dir, "EnumerateTest.json"))
adaptor = EnumlibAdaptor(struct, 1, 1)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 2)
def setUp(self):
structure = PymatgenTest.get_structure("LiFePO4")
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
def setUp(self):
structure_dict = {
"lattice": {
"a":
4.754150115,
"volume":
302.935463898643,
"c":
10.462573348,
"b":
6.090300362,
"matrix": [[4.754150115, 0.0, 0.0], [0.0, 6.090300362, 0.0],
[0.0, 0.0, 10.462573348]],
"alpha":
90.0,
"beta":
90.0,
"gamma":
90.0
},
"sites": [{
"occu": 1,
"abc": [0.0, 0.0, 0.0],
"xyz": [0.0, 0.0, 0.0],
"species": [{
"occu": 1,
"element": "Li"
}],
"label": "Li"
}, {
"occu": 1,
"abc": [0.5000010396179928, 0.0, 0.5000003178950235],
"xyz": [2.37708, 0.0, 5.23129],
"species": [{
"occu": 1,
"element": "Li"
}],
"label": "Li"
}, {
"occu": 1,
"abc": [0.0, 0.49999997028061194, 0.0],
"xyz": [0.0, 3.04515, 0.0],
"species": [{
"occu": 1,
"element": "Li"
}],
"label": "Li"
}, {
"occu":
1,
"abc":
[0.5000010396179928, 0.49999997028061194, 0.5000003178950235],
"xyz": [2.37708, 3.04515, 5.23129],
"species": [{
"occu": 1,
"element": "Li"
}],
"label":
"Li"
}, {
"occu":
1,
"abc":
[0.7885825876997996, 0.5473161916279229, 0.3339168944194627],
"xyz": [3.74904, 3.33332, 3.4936300000000005],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.2114173881108085, 0.452683748933301, 0.6660827855827808],
"xyz": [1.00511, 2.75698, 6.968940000000001],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.7114184277288014, 0.5473161916279229, 0.8339172123144861],
"xyz": [3.38219, 3.33332, 8.72492],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.7885825876997996, 0.9526820772587701, 0.3339168944194627],
"xyz": [3.74904, 5.8021199999999995, 3.4936300000000005],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc": [
0.28858365150718424, 0.047317863302453654,
0.16608342347556082
],
"xyz": [1.37197, 0.28818, 1.73766],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.7440972443925447, 0.25000080611787734, 0.09613791622232937],
"xyz": [3.537549999999999, 1.52258, 1.00585],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.28858365150718424, 0.452683748933301, 0.16608342347556082],
"xyz": [1.37197, 2.75698, 1.73766],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.2114173881108085, 0.047317863302453654, 0.6660827855827808],
"xyz": [1.00511, 0.28818, 6.968940000000001],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.2559006279926859, 0.7499991344433464, 0.9038627195677177],
"xyz": [1.21659, 4.56772, 9.45673],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.7559016676106785, 0.25000080611787734, 0.5961372783295493],
"xyz": [3.5936699999999986, 1.52258, 6.2371300000000005],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.7939989080466804, 0.7499991344433464, 0.5421304884886912],
"xyz": [3.77479, 4.56772, 5.67208],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.24409830819992942, 0.7499991344433464, 0.40386240167269416],
"xyz": [1.16048, 4.56772, 4.22544],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.7060021073819206, 0.7499991344433464, 0.04213017059366761],
"xyz": [3.35644, 4.56772, 0.44079000000000007],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.2939978684286875, 0.25000080611787734, 0.9578695094085758],
"xyz": [1.3977099999999996, 1.52258, 10.02178],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.20600106776392774, 0.25000080611787734, 0.4578701473013559],
"xyz": [0.9793599999999998, 1.52258, 4.7905],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.7114184277288014, 0.9526820772587701, 0.8339172123144861],
"xyz": [3.38219, 5.8021199999999995, 8.72492],
"species": [{
"occu": 1,
"element": "O"
}],
"label":
"O"
}, {
"occu":
1,
"abc":
[0.5793611756830275, 0.7499991344433464, 0.9051119342269868],
"xyz": [2.75437, 4.56772, 9.4698],
"species": [{
"occu": 1,
"element": "P"
}],
"label":
"P"
}, {
"occu":
1,
"abc":
[0.9206377363201961, 0.7499991344433464, 0.40511161633196324],
"xyz": [4.37685, 4.56772, 4.23851],
"species": [{
"occu": 1,
"element": "P"
}],
"label":
"P"
}, {
"occu":
1,
"abc": [
0.42063880012758065, 0.25000080611787734,
0.09488774577525667
],
"xyz": [1.9997799999999994, 1.52258, 0.99277],
"species": [{
"occu": 1,
"element": "P"
}],
"label":
"P"
}, {
"occu":
1,
"abc":
[0.07936223949041206, 0.25000080611787734, 0.5948880636702801],
"xyz": [0.3773, 1.52258, 6.22406],
"species": [{
"occu": 1,
"element": "P"
}],
"label":
"P"
}, {
"occu":
1,
"abc":
[0.021860899947623972, 0.7499991344433464, 0.7185507570598875],
"xyz": [0.10393, 4.56772, 7.517890000000001],
"species": [{
"occu": 1,
"element": "Fe"
}],
"label":
"Fe"
}, {
"occu":
1,
"abc":
[0.478135932819614, 0.7499991344433464, 0.21855043916486389],
"xyz": [2.27313, 4.56772, 2.2866],
"species": [{
"occu": 1,
"element": "Fe"
}],
"label":
"Fe"
}, {
"occu":
1,
"abc":
[0.9781369724376069, 0.25000080611787734, 0.2814489229423561],
"xyz": [4.65021, 1.52258, 2.9446800000000004],
"species": [{
"occu": 1,
"element": "Fe"
}],
"label":
"Fe"
}, {
"occu":
1,
"abc":
[0.5218619395656168, 0.25000080611787734, 0.7814492408373795],
"xyz": [2.48101, 1.52258, 8.17597],
"species": [{
"occu": 1,
"element": "Fe"
}],
"label":
"Fe"
}]
}
structure = Structure.from_dict(structure_dict)
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
def pred_from_structures(self, target_species, structures_list,
remove_duplicates=True):
"""
performs a structure prediction targeting compounds containing the
target_species and based on a list of structure (those structures
can for instance come from a database like the ICSD). It will return
all the structures formed by ionic substitutions with a probability
higher than the threshold
Args:
target_species:
a list of species with oxidation states
e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)]
structures_list:
a list of dictionnary of the form {'structure':Structure object
,'id':some id where it comes from}
the id can for instance refer to an ICSD id
Returns:
a list of TransformedStructure objects.
"""
result = []
transmuter = StandardTransmuter([])
if len(list(set(target_species) & set(self.get_allowed_species()))) \
!= len(target_species):
raise ValueError("the species in target_species are not allowed"
+ "for the probability model you are using")
for permut in itertools.permutations(target_species):
for s in structures_list:
#check if: species are in the domain,
#and the probability of subst. is above the threshold
els = s['structure'].composition.elements
if len(els) == len(permut) and \
len(list(set(els) & set(self.get_allowed_species()))) == \
len(els) and self._sp.cond_prob_list(permut, els) > \
self._threshold:
clean_subst = {els[i]: permut[i]
for i in xrange(0, len(els))
if els[i] != permut[i]}
if len(clean_subst) == 0:
continue
transf = SubstitutionTransformation(clean_subst)
if Substitutor._is_charge_balanced(
transf.apply_transformation(s['structure'])):
ts = TransformedStructure(
s['structure'], [transf], history=[s['id']],
other_parameters={
'type': 'structure_prediction',
'proba': self._sp.cond_prob_list(permut, els)}
)
result.append(ts)
transmuter.append_transformed_structures([ts])
if remove_duplicates:
transmuter.apply_filter(RemoveDuplicatesFilter(
symprec=self._symprec))
return transmuter.transformed_structures
def test_init(self):
test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..",
'test_files')
parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
struct = parser.get_structures(False)[0]
subtrans = SubstitutionTransformation({'Li': {'Li': 0.5}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 86)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")), 0.5 / 6.5)
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
2,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 52)
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
1,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 1)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")), 0.25 / 6.25)
#Make sure it works for completely disordered structures.
struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{
'Fe': 0.5
}], [[0, 0, 0]])
adaptor = EnumlibAdaptor(struct, 1, 2)
adaptor.run()
self.assertEqual(len(adaptor.structures), 3)
#Make sure it works properly when symmetry is broken by ordered sites.
parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
struct = parser.get_structures(False)[0]
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
s = subtrans.apply_transformation(struct)
#REmove some ordered sites to break symmetry.
removetrans = RemoveSitesTransformation([4, 7])
s = removetrans.apply_transformation(s)
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 4)
struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]], [{
"Si": 0.5
}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
adaptor = EnumlibAdaptor(struct, 1, 3, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 10)
from pymatgen.io.cif import CifParser
from pymatgen.transformations.standard_transformations import RemoveSpeciesTransformation
from pymatgen.transformations.standard_transformations import SubstitutionTransformation
if __name__ == '__main__':
# Read in a LiFePO4 structure from a cif.
parser = CifParser('/Users/derek/Downloads/LiFePO4_mp-19017_computed.cif')
struct = parser.get_structures()[0]
t = RemoveSpeciesTransformation(["Li"])
modified_structure = t.apply_transformation(struct)
t2 = SubstitutionTransformation({"Li", "Na"})
print(modified_structure)
from matplotlib.testing.compare import compare_images
from pymatgen.core import Lattice, Structure
from pymatgen.transformations.standard_transformations import SubstitutionTransformation
from pymatviz.struct_vis import plot_structure_2d
from .conftest import save_reference_img
os.makedirs(fixt_dir := "tests/fixtures/struct_vis", exist_ok=True)
latt = Lattice.cubic(5)
struct = Structure(latt, ["Fe", "O"], [[0, 0, 0], [0.5, 0.5, 0.5]])
disord_struct: Structure = SubstitutionTransformation({
"Fe": {
"Fe": 0.75,
"C": 0.25
}
}).apply_transformation(struct)
@pytest.mark.parametrize("radii", [0.5, 1.2])
@pytest.mark.parametrize("rot", ["0x,0y,0z", "10x,-10y,0z"])
@pytest.mark.parametrize("labels", [True, False, {"P": "Phosphor"}])
def test_plot_structure_2d(radii, rot, labels, tmpdir):
# set explicit size to avoid ImageComparisonFailure in CI: sizes do not match
# expected (700, 1350, 3), actual (480, 640, 3)
plt.figure(figsize=(5, 5))
ax = plot_structure_2d(disord_struct,
atomic_radii=radii,
rotation=rot,
key=lambda item: item[1])
}
tar_d = {
k: v
for k, v in sorted(Composition(composition).as_dict().items(),
key=lambda item: item[1])
}
ori_t = tuple(ori_d)
tar_t = tuple(tar_d)
# print('original composition: ' + str(ori_t))
# print('target composition: ' + str(tar_t))
replace_syntax = find_diff(ori_t, tar_t)
trans = []
for syn in replace_syntax:
trans.append(SubstitutionTransformation(syn))
# os.system("cd ./CIF")
# print(os.getcwd())
try:
transmuter = CifTransmuter.from_filenames(
['./renamed_cif/' + str(mpid) + ".cif"], trans)
structures = transmuter.transformed_structures
#print(structures[0].final_structure)
w = CifWriter(structures[0].final_structure)
w.write_file("./inverse_design_target/" + name)
except:
continue
print('success!!')
# parser = CifParser('./file_name.cif')
from pymatgen import Structure
from pymatgen.transformations.standard_transformations import SubstitutionTransformation
from pymatgen.transformations.standard_transformations import OrderDisorderedStructureTransformation
structure = Structure.from_file("POSCAR")
substitution = SubstitutionTransformation({"Nb3+": {"Nb3+":0.5, "Fe3+":0.5}})
result = substitution.apply_transformation(structure)
order = OrderDisorderedStructureTransformation(algo=2)
ResultOrder = order.apply_transformation(result, return_ranked_list=True)
for i, item in enumerate(ResultOrder):
item['structure'].to(filename="POSCAR{:02d}".format(i))
#ResultOrder[0]['structure'].to(filename="POSCAR1")