Esempio n. 1
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 def test_min_relative_distances(self):
     self.assertAlmostEqual(MinimumRelativeDistances().featurize(
             self.diamond_no_oxi)[0][0], 1.1052576)
     self.assertAlmostEqual(MinimumRelativeDistances().featurize(
             self.nacl)[0][0], 0.8891443)
     self.assertAlmostEqual(MinimumRelativeDistances().featurize(
             self.cscl)[0][0], 0.9877540)
Esempio n. 2
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 def test_min_relative_distances(self):
     self.assertAlmostEqual(
         int(1000 * MinimumRelativeDistances().featurize(
             self.diamond_no_oxi)[0][0]), 1105)
     self.assertAlmostEqual(
         int(1000 * MinimumRelativeDistances().featurize(self.nacl)[0][0]),
         1005)
     self.assertAlmostEqual(
         int(1000 * MinimumRelativeDistances().featurize(self.cscl)[0][0]),
         1006)
Esempio n. 3
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def predict_log10_eps(
    target: Union[Structure, Composition],
    dielectric_type: str,
    model_type: str,
) -> float:
    """
    :param target: structure or composition to predict dielectric constants
    :param dielectric_type: "el" or "ion"
    :param model_type: "comp" or "comp_st"
    :return: Descriptor vector
    """
    if dielectric_type not in ["el", "ion"]:
        raise ValueError(
            f'Specify dielectric type "el" or "ion"\nInput: {dielectric_type}')
    if model_type not in ["comp", "comp_st"]:
        raise ValueError(
            f'Specify regression_type "comp" or "comp_st"\nInput: {model_type}'
        )

    if model_type == "comp":
        if isinstance(target, Structure):
            comp = target.composition
        else:
            comp = target
        comp_oxi = comp.add_charges_from_oxi_state_guesses()
        if dielectric_type == "el":
            ep = ScalarFeaturizer(ElementProperty.from_preset("matminer"),
                                  comp)
            valence = ScalarFeaturizer(ValenceOrbital(), comp)
            ion_prop = ScalarFeaturizer(IonProperty(), comp)
            en_diff = ScalarFeaturizer(ElectronegativityDiff(), comp_oxi)
            oxi_state = ScalarFeaturizer(OxidationStates.from_preset("deml"),
                                         comp_oxi)
            atomic_orbital = ScalarFeaturizer(AtomicOrbitals(), comp)
            descriptor = [
                ep.get_from_label("PymatgenData minimum X"),
                ep.get_from_label("PymatgenData range X"),
                ep.get_from_label("PymatgenData std_dev X"),
                ep.get_from_label("PymatgenData mean row"),
                ep.get_from_label("PymatgenData std_dev row"),
                ep.get_from_label("PymatgenData mean group"),
                ep.get_from_label("PymatgenData mean block"),
                ep.get_from_label("PymatgenData std_dev block"),
                ep.get_from_label("PymatgenData mean atomic_mass"),
                ep.get_from_label("PymatgenData std_dev atomic_mass"),
                ep.get_from_label("PymatgenData std_dev atomic_radius"),
                ep.get_from_label("PymatgenData minimum mendeleev_no"),
                ep.get_from_label("PymatgenData range mendeleev_no"),
                ep.get_from_label("PymatgenData std_dev mendeleev_no"),
                ep.get_from_label("PymatgenData mean thermal_conductivity"),
                ep.get_from_label("PymatgenData std_dev thermal_conductivity"),
                ep.get_from_label("PymatgenData mean melting_point"),
                ep.get_from_label("PymatgenData std_dev melting_point"),
                valence.get_from_label("avg s valence electrons"),
                valence.get_from_label("avg d valence electrons"),
                valence.get_from_label("frac s valence electrons"),
                valence.get_from_label("frac p valence electrons"),
                valence.get_from_label("frac d valence electrons"),
                ion_prop.get_from_label("avg ionic char"),
                TMetalFraction().featurize(comp)[0],
                en_diff.get_from_label("maximum EN difference"),
                en_diff.get_from_label("range EN difference"),
                en_diff.get_from_label("mean EN difference"),
                en_diff.get_from_label("std_dev EN difference"),
                BandCenter().featurize(comp)[0],
                oxi_state.get_from_label("std_dev oxidation state"),
                atomic_orbital.get_from_label("HOMO_energy"),
                atomic_orbital.get_from_label("LUMO_energy"),
                atomic_orbital.get_from_label("gap_AO"),
            ]
        elif dielectric_type == "ion":
            stoich = ScalarFeaturizer(Stoichiometry(), comp)
            ep = ScalarFeaturizer(ElementProperty.from_preset("matminer"),
                                  comp)
            valence = ScalarFeaturizer(ValenceOrbital(), comp)
            ion_prop = ScalarFeaturizer(IonProperty(), comp)
            en_diff = ScalarFeaturizer(ElectronegativityDiff(), comp_oxi)
            oxi_state = ScalarFeaturizer(OxidationStates.from_preset("deml"),
                                         comp_oxi)
            atomic_orbital = ScalarFeaturizer(AtomicOrbitals(), comp)
            at_pack_eff = ScalarFeaturizer(AtomicPackingEfficiency(), comp)
            descriptor = [
                stoich.get_from_label("3-norm"),
                stoich.get_from_label("5-norm"),
                ep.get_from_label("PymatgenData mean X"),
                ep.get_from_label("PymatgenData mean row"),
                ep.get_from_label("PymatgenData std_dev row"),
                ep.get_from_label("PymatgenData std_dev group"),
                ep.get_from_label("PymatgenData mean block"),
                ep.get_from_label("PymatgenData std_dev block"),
                ep.get_from_label("PymatgenData maximum atomic_mass"),
                ep.get_from_label("PymatgenData range atomic_mass"),
                ep.get_from_label("PymatgenData mean atomic_mass"),
                ep.get_from_label("PymatgenData std_dev atomic_mass"),
                ep.get_from_label("PymatgenData maximum atomic_radius"),
                ep.get_from_label("PymatgenData range atomic_radius"),
                ep.get_from_label("PymatgenData mean atomic_radius"),
                ep.get_from_label("PymatgenData std_dev atomic_radius"),
                ep.get_from_label("PymatgenData minimum mendeleev_no"),
                ep.get_from_label("PymatgenData mean mendeleev_no"),
                ep.get_from_label("PymatgenData std_dev mendeleev_no"),
                ep.get_from_label("PymatgenData mean thermal_conductivity"),
                ep.get_from_label("PymatgenData std_dev thermal_conductivity"),
                ep.get_from_label("PymatgenData mean melting_point"),
                ep.get_from_label("PymatgenData std_dev melting_point"),
                valence.get_from_label("avg s valence electrons"),
                valence.get_from_label("frac s valence electrons"),
                valence.get_from_label("frac p valence electrons"),
                valence.get_from_label("frac d valence electrons"),
                ion_prop.get_from_label("avg ionic char"),
                TMetalFraction().featurize(comp)[0],
                en_diff.get_from_label("minimum EN difference"),
                en_diff.get_from_label("range EN difference"),
                en_diff.get_from_label("mean EN difference"),
                en_diff.get_from_label("std_dev EN difference"),
                oxi_state.get_from_label("range oxidation state"),
                oxi_state.get_from_label("std_dev oxidation state"),
                atomic_orbital.get_from_label("LUMO_energy"),
                atomic_orbital.get_from_label("gap_AO"),
                at_pack_eff.get_from_label("mean simul. packing efficiency"),
                at_pack_eff.get_from_label(
                    "mean abs simul. packing efficiency"),
                at_pack_eff.get_from_label(
                    "dist from 1 clusters |APE| < 0.010"),
                at_pack_eff.get_from_label(
                    "dist from 3 clusters |APE| < 0.010"),
                at_pack_eff.get_from_label(
                    "dist from 5 clusters |APE| < 0.010"),
            ]
    elif model_type == "comp_st":
        if isinstance(target, Composition):
            raise ValueError(
                'comp_st (Using compositional and structural descriptor) is specified, '
                'but target is composition')
        comp: Composition = target.composition
        comp_oxi = comp.add_charges_from_oxi_state_guesses()
        target_orig = deepcopy(target)
        target.add_oxidation_state_by_guess()
        if dielectric_type == "el":
            ep = ScalarFeaturizer(ElementProperty.from_preset("matminer"),
                                  comp)
            valence = ScalarFeaturizer(ValenceOrbital(), comp)
            en_diff = ScalarFeaturizer(ElectronegativityDiff(), comp_oxi)
            atomic_orbital = ScalarFeaturizer(AtomicOrbitals(), comp)
            density = ScalarFeaturizer(DensityFeatures(), target)
            dist_btw_nn = MinimumRelativeDistances().featurize(target_orig)
            opsf = SiteFeaturizer(OPSiteFingerprint(), target)
            voro_fp = SiteFeaturizer(VoronoiFingerprint(use_symm_weights=True),
                                     target)
            gsf = SiteFeaturizer(GaussianSymmFunc(), target)
            lpd = SiteFeaturizer(
                LocalPropertyDifference.from_preset("ward-prb-2017"), target)
            descriptor = [
                ep.get_from_label("PymatgenData std_dev X"),
                ep.get_from_label("PymatgenData mean block"),
                ep.get_from_label("PymatgenData std_dev atomic_mass"),
                valence.get_from_label("frac d valence electrons"),
                TMetalFraction().featurize(comp)[0],
                en_diff.get_from_label("maximum EN difference"),
                en_diff.get_from_label("mean EN difference"),
                atomic_orbital.get_from_label("HOMO_energy"),
                atomic_orbital.get_from_label("LUMO_energy"),
                density.get_from_label("density"),
                np.mean(dist_btw_nn),
                np.std(dist_btw_nn),
                opsf.get_from_label_func("tetrahedral CN_4", np.max),
                opsf.get_from_label_func("rectangular see-saw-like CN_4",
                                         np.max),
                np.max([
                    EwaldSiteEnergy(accuracy=4).featurize(target, i)
                    for i in range(target.num_sites)
                ]),
                voro_fp.get_from_label_func("Voro_area_std_dev", np.max),
                voro_fp.get_from_label_func("Voro_area_std_dev", np.mean),
                voro_fp.get_from_label_func("Voro_dist_minimum", np.min),
                voro_fp.get_from_label_func("Voro_dist_minimum", np.std),
                gsf.get_from_label_func("G2_20.0", np.std),
                gsf.get_from_label_func("G2_80.0", np.max),
                gsf.get_from_label_func("G4_0.005_4.0_-1.0", np.mean),
                lpd.get_from_label_func("local difference in NdValence",
                                        np.mean),
                lpd.get_from_label_func("local difference in NValence",
                                        np.min),
                lpd.get_from_label_func("local difference in NValence",
                                        np.std),
                lpd.get_from_label_func("local difference in NdUnfilled",
                                        np.mean),
                lpd.get_from_label_func("local difference in NUnfilled",
                                        np.min),
                lpd.get_from_label_func("local difference in NUnfilled",
                                        np.mean),
                lpd.get_from_label_func("local difference in GSmagmom",
                                        np.mean)
            ]
        elif dielectric_type == "ion":
            ep = ScalarFeaturizer(ElementProperty.from_preset("matminer"),
                                  comp)
            atomic_orbitals = ScalarFeaturizer(AtomicOrbitals(), comp)
            density = ScalarFeaturizer(DensityFeatures(), target)
            str_het = ScalarFeaturizer(StructuralHeterogeneity(), target)
            opsf = SiteFeaturizer(OPSiteFingerprint(), target)
            voro_fp = SiteFeaturizer(VoronoiFingerprint(use_symm_weights=True),
                                     target)
            gsf = SiteFeaturizer(GaussianSymmFunc(), target)
            lpd = SiteFeaturizer(
                LocalPropertyDifference.from_preset("ward-prb-2017"), target)
            descriptor = [
                ep.get_from_label("PymatgenData std_dev row"),
                ep.get_from_label("PymatgenData mean thermal_conductivity"),
                ep.get_from_label("PymatgenData std_dev melting_point"),
                TMetalFraction().featurize(comp)[0],
                atomic_orbitals.get_from_label("gap_AO"),
                density.get_from_label("density"),
                density.get_from_label("packing fraction"),
                str_het.get_from_label("mean neighbor distance variation"),
                str_het.get_from_label("avg_dev neighbor distance variation"),
                opsf.get_from_label_func("sgl_bd CN_1", np.mean),
                opsf.get_from_label_func("bent 150 degrees CN_2", np.mean),
                opsf.get_from_label_func("linear CN_2", np.mean),
                opsf.get_from_label_func("trigonal planar CN_3", np.mean),
                opsf.get_from_label_func("pentagonal planar CN_5", np.std),
                opsf.get_from_label_func("octahedral CN_6", np.max),
                opsf.get_from_label_func("octahedral CN_6", np.std),
                opsf.get_from_label_func("q6 CN_12", np.mean),
                np.max([
                    EwaldSiteEnergy(accuracy=4).featurize(target, i)
                    for i in range(target.num_sites)
                ]),
                voro_fp.get_from_label_func("Symmetry_weighted_index_4",
                                            np.std),
                voro_fp.get_from_label_func("Voro_vol_maximum", np.mean),
                voro_fp.get_from_label_func("Voro_area_std_dev", np.mean),
                voro_fp.get_from_label_func("Voro_area_minimum", np.std),
                voro_fp.get_from_label_func("Voro_area_maximum", np.min),
                voro_fp.get_from_label_func("Voro_dist_std_dev", np.mean),
                gsf.get_from_label_func("G2_80.0", np.min),
                gsf.get_from_label_func("G4_0.005_4.0_1.0", np.std),
                lpd.get_from_label_func("local difference in Number", np.max),
                lpd.get_from_label_func("local difference in MendeleevNumber",
                                        np.max),
                lpd.get_from_label_func("local difference in MendeleevNumber",
                                        np.min),
                lpd.get_from_label_func("local difference in AtomicWeight",
                                        np.max),
                lpd.get_from_label_func("local difference in AtomicWeight",
                                        np.mean),
                lpd.get_from_label_func("local difference in MeltingT",
                                        np.mean),
                lpd.get_from_label_func("local difference in Row", np.max),
                lpd.get_from_label_func(
                    "local difference in Electronegativity", np.min),
                lpd.get_from_label_func("local difference in NValence",
                                        np.std),
                lpd.get_from_label_func("local difference in NsUnfilled",
                                        np.mean),
                lpd.get_from_label_func("local difference in NdUnfilled",
                                        np.max),
                lpd.get_from_label_func("local difference in NdUnfilled",
                                        np.std),
                lpd.get_from_label_func("local difference in NUnfilled",
                                        np.max),
                lpd.get_from_label_func("local difference in NUnfilled",
                                        np.min),
                lpd.get_from_label_func("local difference in NUnfilled",
                                        np.mean),
                lpd.get_from_label_func("local difference in NUnfilled",
                                        np.std),
                lpd.get_from_label_func("local difference in GSvolume_pa",
                                        np.max),
                lpd.get_from_label_func("local difference in GSvolume_pa",
                                        np.min),
                lpd.get_from_label_func("local difference in SpaceGroupNumber",
                                        np.max),
            ]
    with open(
            f"{os.path.dirname(__file__)}/{dielectric_type}_{model_type}.joblib",
            "rb") as fr:
        model: RandomForestRegressor = joblib.load(fr)
    with open(
            f"{os.path.dirname(__file__)}/{dielectric_type}_{model_type}_scaler.joblib",
            "rb") as fr:
        scaler: StandardScaler = joblib.load(fr)
    descriptor = scaler.transform([descriptor])
    return model.predict(descriptor)[0]
def AddFeatures(df):  # Add features by Matminer
    from matminer.featurizers.conversions import StrToComposition
    df = StrToComposition().featurize_dataframe(df, "formula")

    from matminer.featurizers.composition import ElementProperty

    ep_feat = ElementProperty.from_preset(preset_name="magpie")
    df = ep_feat.featurize_dataframe(
        df, col_id="composition"
    )  # input the "composition" column to the featurizer

    from matminer.featurizers.conversions import CompositionToOxidComposition
    from matminer.featurizers.composition import OxidationStates

    df = CompositionToOxidComposition().featurize_dataframe(df, "composition")

    os_feat = OxidationStates()
    df = os_feat.featurize_dataframe(df, "composition_oxid")

    from matminer.featurizers.composition import ElectronAffinity

    ea_feat = ElectronAffinity()
    df = ea_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import BandCenter

    bc_feat = BandCenter()
    df = bc_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import CohesiveEnergy

    ce_feat = CohesiveEnergy()
    df = ce_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import Miedema

    m_feat = Miedema()
    df = m_feat.featurize_dataframe(df, "composition_oxid", ignore_errors=True)

    from matminer.featurizers.composition import TMetalFraction

    tmf_feat = TMetalFraction()
    df = tmf_feat.featurize_dataframe(df,
                                      "composition_oxid",
                                      ignore_errors=True)

    from matminer.featurizers.composition import ValenceOrbital

    vo_feat = ValenceOrbital()
    df = vo_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import YangSolidSolution

    yss_feat = YangSolidSolution()
    df = yss_feat.featurize_dataframe(df,
                                      "composition_oxid",
                                      ignore_errors=True)

    from matminer.featurizers.structure import GlobalSymmetryFeatures

    # This is the border between compositional features and structural features. Comment out the following featurizers to use only compostional features.

    gsf_feat = GlobalSymmetryFeatures()
    df = gsf_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import StructuralComplexity
    sc_feat = StructuralComplexity()
    df = sc_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import ChemicalOrdering
    co_feat = ChemicalOrdering()
    df = co_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import MaximumPackingEfficiency
    mpe_feat = MaximumPackingEfficiency()
    df = mpe_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import MinimumRelativeDistances
    mrd_feat = MinimumRelativeDistances()
    df = mrd_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import StructuralHeterogeneity
    sh_feat = StructuralHeterogeneity()
    df = sh_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import SiteStatsFingerprint

    from matminer.featurizers.site import AverageBondLength
    from pymatgen.analysis.local_env import CrystalNN
    bl_feat = SiteStatsFingerprint(
        AverageBondLength(CrystalNN(search_cutoff=20)))
    df = bl_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.site import AverageBondAngle
    ba_feat = SiteStatsFingerprint(
        AverageBondAngle(CrystalNN(search_cutoff=20)))
    df = ba_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.site import BondOrientationalParameter
    bop_feat = SiteStatsFingerprint(BondOrientationalParameter())
    df = bop_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.site import CoordinationNumber
    cn_feat = SiteStatsFingerprint(CoordinationNumber())
    df = cn_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import DensityFeatures
    df_feat = DensityFeatures()
    df = df_feat.featurize_dataframe(df, "structure", ignore_errors=True)
    return (df)