def test_apply_transformations_best_first(self):
p = Poscar.from_file(os.path.join(PymatgenTest.TEST_FILES_DIR,
"POSCAR.LiFePO4"),
check_for_POTCAR=False)
t1 = OxidationStateDecorationTransformation({
"Li": 1,
"Fe": 2,
"P": 5,
"O": -2
})
s = t1.apply_transformation(p.structure)
t = PartialRemoveSpecieTransformation(
"Li+", 0.5, PartialRemoveSpecieTransformation.ALGO_BEST_FIRST)
self.assertEqual(len(t.apply_transformation(s)), 26)
def test_transmute(self):
if QeTransmuterTest.qe is None:
self.skipTest("No MongoDB present")
crit = {}
trans = [
SubstitutionTransformation({"Zn": "Mg"}),
OxidationStateDecorationTransformation({
"B": 3,
"O": -2,
"Mg": 2,
"Tb": 3
}),
PartialRemoveSpecieTransformation(
"Mg2+",
0.5,
algo=PartialRemoveSpecieTransformation.ALGO_COMPLETE)
]
self.qep = QeTransmuter(QeTransmuterTest.qe,
crit,
trans,
extend_collection=10)
trans_structures = self.qep.transformed_structures
self.assertEqual(len(trans_structures), 3)
for s in trans_structures:
self.assertEqual(s.final_structure.composition.reduced_formula,
"Tb2Mg(BO2)10")
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_apply_transformation(self):
t = PartialRemoveSpecieTransformation("Li+", 1.0 / 3, 3)
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+", "Li+", "O2-"], coords)
self.assertEqual(len(t.apply_transformation(struct, 100)), 2)
d = t.as_dict()
self.assertEqual(
type(PartialRemoveSpecieTransformation.from_dict(d)),
PartialRemoveSpecieTransformation,
)
def test_apply_transformation_fast(self):
t = PartialRemoveSpecieTransformation("Li+", 0.5)
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])
coords.append([0.1, 0.1, 0.1])
coords.append([0.3, 0.75, 0.3])
lattice = Lattice([[10, 0.00, 0.00], [0, 10, 0.00], [0.00, 0, 10]])
struct = Structure(lattice, ["Li+"] * 6, coords)
fast_opt_s = t.apply_transformation(struct)
t = PartialRemoveSpecieTransformation(
"Li+", 0.5, PartialRemoveSpecieTransformation.ALGO_COMPLETE)
slow_opt_s = t.apply_transformation(struct)
self.assertAlmostEqual(
EwaldSummation(fast_opt_s).total_energy,
EwaldSummation(slow_opt_s).total_energy,
4,
)
self.assertEqual(fast_opt_s, slow_opt_s)
structure.add_oxidation_state_by_guess()
len = structure.composition.as_dict()[ion]
if len > 26 :
print(" WARNING !! .. too many combinations, might take a long time")
elif len < 3:
print('Not enough combinations. Consider converting into a supercell')
fact = structure.composition.get_reduced_composition_and_factor()[1]
print(f'The given compound has {fact} formula units.')
os.chdir(file)
try:
print(f"{file} with {len} {ion} has initiated.")
for i in np.arange(0,len+1,1):
trans = PartialRemoveSpecieTransformation(ion,i/len)
substructures = trans.apply_transformation(structure,return_ranked_list=1) # change this number to get multiple configurations with higher ewald summation.
substructures[0]['structure'].to(fmt='poscar', filename=str('POSCAR'+str(int(i))))
data.append([i,(1-i/len),substructures[0]['energy'],substructures[0]['energy']/fact]) # the last term is the normatised ewald summation with number of formula units
df=pd.DataFrame(data,columns=['file','composition','Ewald_energy','Norm ( Ewald_energy / '+str(fact)+')' ])
df.to_csv(file+'.csv')
print(f'{file} saved')
except:
print(' Operation failed to find possible structures.')
except:
print('Operation failed to recognise oxidation states of the elements. The CIF file should contain oxidation states.')