Esempio n. 1
0
    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
Esempio n. 2
0
 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)
Esempio n. 3
0
 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")
Esempio n. 4
0
    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
Esempio n. 5
0
    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)
Esempio n. 6
0
 def setUp(self):
     structure = PymatgenTest.get_structure("LiFePO4")
     self.structure = structure
     trans = [SubstitutionTransformation({"Li": "Na"})]
     self.trans = TransformedStructure(structure, trans)
Esempio n. 7
0
 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)
Esempio n. 8
0
    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
Esempio n. 9
0
    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)
Esempio n. 10
0
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)
Esempio n. 11
0
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')
Esempio n. 13
0
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")