def test_voronoifingerprint(self):
df_sc = pd.DataFrame({'struct': [self.sc], 'site': [0]})
vorofp = VoronoiFingerprint(use_symm_weights=True)
vorofps = vorofp.featurize_dataframe(df_sc, ['struct', 'site'])
self.assertAlmostEqual(vorofps['Voro_index_3'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_4'][0], 6.0)
self.assertAlmostEqual(vorofps['Voro_index_5'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_6'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_7'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_8'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_9'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_10'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_3'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_4'][0], 1.0)
self.assertAlmostEqual(vorofps['Symmetry_index_5'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_6'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_7'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_8'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_9'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_10'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_3'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_4'][0], 1.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_5'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_6'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_7'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_8'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_9'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_10'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_vol_sum'][0], 43.614208)
self.assertAlmostEqual(vorofps['Voro_area_sum'][0], 74.3424)
self.assertAlmostEqual(vorofps['Voro_vol_mean'][0], 7.269034667)
self.assertAlmostEqual(vorofps['Voro_vol_std_dev'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_vol_minimum'][0], 7.269034667)
self.assertAlmostEqual(vorofps['Voro_vol_maximum'][0], 7.269034667)
self.assertAlmostEqual(vorofps['Voro_area_mean'][0], 12.3904)
self.assertAlmostEqual(vorofps['Voro_area_std_dev'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_area_minimum'][0], 12.3904)
self.assertAlmostEqual(vorofps['Voro_area_maximum'][0], 12.3904)
self.assertAlmostEqual(vorofps['Voro_dist_mean'][0], 3.52)
self.assertAlmostEqual(vorofps['Voro_dist_std_dev'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_dist_minimum'][0], 3.52)
self.assertAlmostEqual(vorofps['Voro_dist_maximum'][0], 3.52)
def test_voronoifingerprint(self):
df_sc= pd.DataFrame({'struct': [self.sc], 'site': [0]})
vorofp = VoronoiFingerprint(use_symm_weights=True)
vorofps = vorofp.featurize_dataframe(df_sc, ['struct', 'site'])
self.assertAlmostEqual(vorofps['Voro_index_3'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_4'][0], 6.0)
self.assertAlmostEqual(vorofps['Voro_index_5'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_6'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_7'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_8'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_9'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_index_10'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_3'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_4'][0], 1.0)
self.assertAlmostEqual(vorofps['Symmetry_index_5'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_6'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_7'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_8'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_9'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_index_10'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_3'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_4'][0], 1.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_5'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_6'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_7'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_8'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_9'][0], 0.0)
self.assertAlmostEqual(vorofps['Symmetry_weighted_index_10'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_vol_sum'][0], 43.614208)
self.assertAlmostEqual(vorofps['Voro_area_sum'][0], 74.3424)
self.assertAlmostEqual(vorofps['Voro_vol_mean'][0], 7.269034667)
self.assertAlmostEqual(vorofps['Voro_vol_std_dev'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_vol_minimum'][0], 7.269034667)
self.assertAlmostEqual(vorofps['Voro_vol_maximum'][0], 7.269034667)
self.assertAlmostEqual(vorofps['Voro_area_mean'][0], 12.3904)
self.assertAlmostEqual(vorofps['Voro_area_std_dev'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_area_minimum'][0], 12.3904)
self.assertAlmostEqual(vorofps['Voro_area_maximum'][0], 12.3904)
self.assertAlmostEqual(vorofps['Voro_dist_mean'][0], 3.52)
self.assertAlmostEqual(vorofps['Voro_dist_std_dev'][0], 0.0)
self.assertAlmostEqual(vorofps['Voro_dist_minimum'][0], 3.52)
self.assertAlmostEqual(vorofps['Voro_dist_maximum'][0], 3.52)
class DeBreuck2020Featurizer(modnet.featurizers.MODFeaturizer):
""" Featurizer presets used for the paper 'Machine learning
materials properties for small datasets' by Pierre-Paul De Breuck,
Geoffroy Hautier & Gian-Marco Rignanese, arXiv:2004.14766 (2020).
Uses most of the featurizers implemented by matminer at the time of
writing with their default hyperparameters and presets.
"""
from matminer.featurizers.composition import (
AtomicOrbitals,
AtomicPackingEfficiency,
BandCenter,
# CohesiveEnergy, - This descriptor was not used in the paper preset
# ElectronAffinity, - This descriptor was not used in the paper preset
ElectronegativityDiff,
ElementFraction,
ElementProperty,
IonProperty,
Miedema,
OxidationStates,
Stoichiometry,
TMetalFraction,
ValenceOrbital,
YangSolidSolution,
)
from matminer.featurizers.structure import (
# BagofBonds, - This descriptor was not used in the paper preset
BondFractions,
ChemicalOrdering,
CoulombMatrix,
DensityFeatures,
EwaldEnergy,
GlobalSymmetryFeatures,
MaximumPackingEfficiency,
# PartialRadialDistributionFunction,
RadialDistributionFunction,
SineCoulombMatrix,
StructuralHeterogeneity,
XRDPowderPattern,
)
from matminer.featurizers.site import (
AGNIFingerprints,
AverageBondAngle,
AverageBondLength,
BondOrientationalParameter,
ChemEnvSiteFingerprint,
CoordinationNumber,
CrystalNNFingerprint,
GaussianSymmFunc,
GeneralizedRadialDistributionFunction,
LocalPropertyDifference,
OPSiteFingerprint,
VoronoiFingerprint,
)
composition_featurizers = (
AtomicOrbitals(),
AtomicPackingEfficiency(),
BandCenter(),
ElementFraction(),
ElementProperty.from_preset("magpie"),
IonProperty(),
Miedema(),
Stoichiometry(),
TMetalFraction(),
ValenceOrbital(),
YangSolidSolution(),
)
oxide_composition_featurizers = (
ElectronegativityDiff(),
OxidationStates(),
)
structure_featurizers = (
DensityFeatures(),
GlobalSymmetryFeatures(),
RadialDistributionFunction(),
CoulombMatrix(),
# PartialRadialDistributionFunction(),
SineCoulombMatrix(),
EwaldEnergy(),
BondFractions(),
StructuralHeterogeneity(),
MaximumPackingEfficiency(),
ChemicalOrdering(),
XRDPowderPattern(),
# BagofBonds(),
)
site_featurizers = (
AGNIFingerprints(),
AverageBondAngle(VoronoiNN()),
AverageBondLength(VoronoiNN()),
BondOrientationalParameter(),
ChemEnvSiteFingerprint.from_preset("simple"),
CoordinationNumber(),
CrystalNNFingerprint.from_preset("ops"),
GaussianSymmFunc(),
GeneralizedRadialDistributionFunction.from_preset("gaussian"),
LocalPropertyDifference(),
OPSiteFingerprint(),
VoronoiFingerprint(),
)
def featurize_composition(self, df):
""" Applies the preset composition featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_composition(df)
_orbitals = {"s": 1, "p": 2, "d": 3, "f": 4}
df['AtomicOrbitals|HOMO_character'] = df[
'AtomicOrbitals|HOMO_character'].map(_orbitals)
df['AtomicOrbitals|LUMO_character'] = df[
'AtomicOrbitals|LUMO_character'].map(_orbitals)
df['AtomicOrbitals|HOMO_element'] = df[
'AtomicOrbitals|HOMO_element'].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z)
df['AtomicOrbitals|LUMO_element'] = df[
'AtomicOrbitals|LUMO_element'].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z)
df = df.replace([np.inf, -np.inf, np.nan], 0)
return modnet.featurizers.clean_df(df)
def featurize_structure(self, df):
""" Applies the preset structural featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_structure(df)
dist = df[
"RadialDistributionFunction|radial distribution function"].iloc[0][
'distances'][:50]
for i, d in enumerate(dist):
_rdf_key = "RadialDistributionFunction|radial distribution function|d_{:.2f}".format(
d)
df[_rdf_key] = df[
"RadialDistributionFunction|radial distribution function"].apply(
lambda x: x['distribution'][i])
df = df.drop("RadialDistributionFunction|radial distribution function",
axis=1)
_crystal_system = {
"cubic": 1,
"tetragonal": 2,
"orthorombic": 3,
"hexagonal": 4,
"trigonal": 5,
"monoclinic": 6,
"triclinic": 7
}
def _int_map(x):
if x == np.nan:
return 0
elif x:
return 1
else:
return 0
df["GlobalSymmetryFeatures|crystal_system"] = df[
"GlobalSymmetryFeatures|crystal_system"].map(_crystal_system)
df["GlobalSymmetryFeatures|is_centrosymmetric"] = df[
"GlobalSymmetryFeatures|is_centrosymmetric"].map(_int_map)
return modnet.featurizers.clean_df(df)
def featurize_site(self, df):
""" Applies the preset site featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
# rename some features for backwards compatibility with pretrained models
aliases = {
"GeneralizedRadialDistributionFunction": "GeneralizedRDF",
"AGNIFingerprints": "AGNIFingerPrint",
"BondOrientationalParameter": "BondOrientationParameter",
"GaussianSymmFunc": "ChemEnvSiteFingerprint|GaussianSymmFunc",
}
df = super().featurize_site(df, aliases=aliases)
df = df.loc[:, (df != 0).any(axis=0)]
return modnet.featurizers.clean_df(df)
class FUTURE_PROSPECTS_2021(featurizer.extendedMODFeaturizer):
from matminer.featurizers.composition import (
AtomicOrbitals,
AtomicPackingEfficiency,
BandCenter,
CohesiveEnergy,
ElectronAffinity,
ElectronegativityDiff,
ElementFraction,
ElementProperty,
IonProperty,
Miedema,
OxidationStates,
Stoichiometry,
TMetalFraction,
ValenceOrbital,
YangSolidSolution,
)
from matminer.featurizers.structure import (
BagofBonds,
BondFractions,
ChemicalOrdering,
CoulombMatrix,
DensityFeatures,
EwaldEnergy,
GlobalSymmetryFeatures,
MaximumPackingEfficiency,
PartialRadialDistributionFunction,
RadialDistributionFunction,
SineCoulombMatrix,
StructuralHeterogeneity,
XRDPowderPattern,
)
from matminer.featurizers.site import (
AGNIFingerprints,
AverageBondAngle,
AverageBondLength,
BondOrientationalParameter,
ChemEnvSiteFingerprint,
CoordinationNumber,
CrystalNNFingerprint,
GaussianSymmFunc,
GeneralizedRadialDistributionFunction,
LocalPropertyDifference,
OPSiteFingerprint,
VoronoiFingerprint,
)
from matminer.featurizers.dos import (
DOSFeaturizer,
SiteDOS,
Hybridization,
DosAsymmetry,
)
from matminer.featurizers.bandstructure import (
BandFeaturizer,
BranchPointEnergy
)
composition_featurizers = (
AtomicOrbitals(),
AtomicPackingEfficiency(),
BandCenter(),
ElementFraction(),
ElementProperty.from_preset("magpie"),
IonProperty(),
Miedema(),
Stoichiometry(),
TMetalFraction(),
ValenceOrbital(),
YangSolidSolution(),
)
oxid_composition_featurizers = (
ElectronegativityDiff(),
OxidationStates(),
)
structure_featurizers = (
DensityFeatures(),
GlobalSymmetryFeatures(),
RadialDistributionFunction(),
CoulombMatrix(),
#PartialRadialDistributionFunction(), #Introduces a large amount of features
SineCoulombMatrix(),
EwaldEnergy(),
BondFractions(),
StructuralHeterogeneity(),
MaximumPackingEfficiency(),
ChemicalOrdering(),
XRDPowderPattern(),
)
site_featurizers = (
AGNIFingerprints(),
AverageBondAngle(VoronoiNN()),
AverageBondLength(VoronoiNN()),
BondOrientationalParameter(),
ChemEnvSiteFingerprint.from_preset("simple"),
CoordinationNumber(),
CrystalNNFingerprint.from_preset("ops"),
GaussianSymmFunc(),
GeneralizedRadialDistributionFunction.from_preset("gaussian"),
LocalPropertyDifference(),
OPSiteFingerprint(),
VoronoiFingerprint(),
)
dos_featurizers = (
DOSFeaturizer(),
SiteDOS(),
Hybridization()
)
band_featurizers = (
BandFeaturizer(),
BranchPointEnergy()
)
def __init__(self, n_jobs=None):
self._n_jobs = n_jobs
def featurize_composition(self, df):
"""Applies the preset composition featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_composition(df)
_orbitals = {"s": 1, "p": 2, "d": 3, "f": 4}
df["AtomicOrbitals|HOMO_character"] = df["AtomicOrbitals|HOMO_character"].map(
_orbitals
)
df["AtomicOrbitals|LUMO_character"] = df["AtomicOrbitals|LUMO_character"].map(
_orbitals
)
df["AtomicOrbitals|HOMO_element"] = df["AtomicOrbitals|HOMO_element"].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z
)
df["AtomicOrbitals|LUMO_element"] = df["AtomicOrbitals|LUMO_element"].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z
)
return clean_df(df)
def featurize_structure(self, df):
"""Applies the preset structural featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_structure(df)
dist = df["RadialDistributionFunction|radial distribution function"].iloc[0][
"distances"
][:50]
for i, d in enumerate(dist):
_rdf_key = "RadialDistributionFunction|radial distribution function|d_{:.2f}".format(
d
)
df[_rdf_key] = df[
"RadialDistributionFunction|radial distribution function"
].apply(lambda x: x["distribution"][i])
df = df.drop("RadialDistributionFunction|radial distribution function", axis=1)
_crystal_system = {
"cubic": 1,
"tetragonal": 2,
"orthorombic": 3,
"hexagonal": 4,
"trigonal": 5,
"monoclinic": 6,
"triclinic": 7,
}
def _int_map(x):
if x == np.nan:
return 0
elif x:
return 1
else:
return 0
df["GlobalSymmetryFeatures|crystal_system"] = df[
"GlobalSymmetryFeatures|crystal_system"
].map(_crystal_system)
df["GlobalSymmetryFeatures|is_centrosymmetric"] = df[
"GlobalSymmetryFeatures|is_centrosymmetric"
].map(_int_map)
return clean_df(df)
def featurize_dos(self, df):
"""Applies the presetdos featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_dos(df)
hotencodeColumns = ["DOSFeaturizer|vbm_specie_1","DOSFeaturizer|cbm_specie_1"]
one_hot = pd.get_dummies(df[hotencodeColumns])
df = df.drop(hotencodeColumns, axis = 1).join(one_hot)
_orbitals = {"s": 1, "p": 2, "d": 3, "f": 4}
df["DOSFeaturizer|vbm_character_1"] = df[
"DOSFeaturizer|vbm_character_1"
].map(_orbitals)
df["DOSFeaturizer|cbm_character_1"] = df[
"DOSFeaturizer|cbm_character_1"
].map(_orbitals)
# Splitting one feature into several floating features
# e.g. number;number;number into three columns
splitColumns = ["DOSFeaturizer|cbm_location_1", "DOSFeaturizer|vbm_location_1"]
for column in splitColumns:
try:
newColumns = df[column].str.split(";", n = 2, expand = True)
for i in range(0,3):
df[column + "_" + str(i)] = np.array(newColumns[i]).astype(np.float)
except:
continue
df = df.drop(splitColumns, axis=1)
df = df.drop(["dos"], axis=1)
return clean_df(df)
def featurize_bandstructure(self, df):
"""Applies the preset band structure featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_bandstructure(df)
def _int_map(x):
if str(x) == "False":
return 0
elif str(x) == "True":
return 1
df["BandFeaturizer|is_gap_direct"] = df[
"BandFeaturizer|is_gap_direct"
].map(_int_map)
df = df.drop(["bandstructure"], axis=1)
return clean_df(df)
def featurize_site(self, df):
"""Applies the preset site featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
aliases = {
"GeneralizedRadialDistributionFunction": "GeneralizedRDF",
"AGNIFingerprints": "AGNIFingerPrint",
"BondOrientationalParameter": "BondOrientationParameter",
"GaussianSymmFunc": "ChemEnvSiteFingerprint|GaussianSymmFunc",
}
df = super().featurize_site(df, aliases=aliases)
df = df.loc[:, (df != 0).any(axis=0)]
return clean_df(df)
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 structure_to_convmol(structure,
properties=ELEMENTAL_PROPERTIES,
max_atoms=200,
max_features=41,
tolerance_distance=0.25):
atomic_radii = {
'At': 1.50,
'Bk': 1.70,
'Cm': 1.74,
'Fr': 2.60,
'He': 0.28,
'Kr': 1.16,
'Lr': 1.71,
'Md': 1.94,
'Ne': 0.58,
'No': 1.97,
'Rn': 1.50,
'Xe': 1.40,
}
distance_matrix = structure.distance_matrix
for index, x in np.ndenumerate(distance_matrix):
radius_1 = Element(structure._sites[
index[0]].specie).atomic_radius or atomic_radii[str(
structure._sites[index[0]].specie)]
radius_2 = Element(structure._sites[
index[1]].specie).atomic_radius or atomic_radii[str(
structure._sites[index[1]].specie)]
max_distance = radius_1 + radius_2 + tolerance_distance
if x > max_distance:
distance_matrix[index] = 0
else:
distance_matrix[index] = 1
np.fill_diagonal(distance_matrix, 1)
atom_features = []
for i, site in enumerate(structure._sites):
atom_feature_vector = []
for atom_property in properties:
min_value = np.nanmin(
np.array(list(atom_property.values()), dtype=float))
max_value = np.nanmax(
np.array(list(atom_property.values()), dtype=float))
if atom_property[str(Element(site.specie))] is not None:
atom_feature_vector.append(
(atom_property[str(Element(site.specie))] - min_value) /
(max_value - min_value))
else:
atom_feature_vector.append(None)
voronoi_min = np.array([
0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0,
1.0
])
voronoi_max = np.array([
120.0, 135.0, 11.0, 3.0, 11.0, 12.0, 18.0, 7.0, 17.0, 17.0, 6.0,
2.0, 6.0, 7.0
])
voronoi_fps = VoronoiFingerprint().featurize(structure, i)
i_fold_symmetry_indices = voronoi_fps[8:16]
voronoi_stats = (np.array(voronoi_fps[16:]) -
voronoi_min) / (voronoi_max - voronoi_min)
atom_feature_vector.extend(i_fold_symmetry_indices +
voronoi_stats.tolist())
coord_min = np.array([1])
coord_max = np.array([36])
coord_fps = (
(CoordinationNumber.from_preset("MinimumDistanceNN").featurize(
structure, i) - coord_min) / (coord_max - coord_min)).tolist()
atom_feature_vector.extend(coord_fps)
atom_features.append(atom_feature_vector)
atom_features = np.array(atom_features, dtype=np.float)
if np.isnan(atom_features).any():
raise ValueError('feature vector contains nan value')
return (zfill(distance_matrix, max_atoms,
max_atoms), zfill(atom_features, max_atoms,
max_features), len(structure.sites))
def featurize_site(df: pd.DataFrame, site_stats=("mean", "std_dev")) -> pd.DataFrame:
""" Decorate input `pandas.DataFrame` of structures with site
features from matminer.
Currently creates the set of all matminer structure features with
the `matminer.featurizers.structure.SiteStatsFingerprint`.
Args:
df (pandas.DataFrame): the input dataframe with `"structure"`
column containing `pymatgen.Structure` objects.
site_stats (Tuple[str]): the matminer site stats to use in the
`SiteStatsFingerprint` for all features.
Returns:
pandas.DataFrame: the decorated DataFrame.
"""
logging.info("Applying site featurizers...")
df = df.copy()
df.columns = ["Input data|" + x for x in df.columns]
site_fingerprints = (
AGNIFingerprints(),
GeneralizedRadialDistributionFunction.from_preset("gaussian"),
OPSiteFingerprint(),
CrystalNNFingerprint.from_preset("ops"),
VoronoiFingerprint(),
GaussianSymmFunc(),
ChemEnvSiteFingerprint.from_preset("simple"),
CoordinationNumber(),
LocalPropertyDifference(),
BondOrientationalParameter(),
AverageBondLength(VoronoiNN()),
AverageBondAngle(VoronoiNN())
)
for fingerprint in site_fingerprints:
site_stats_fingerprint = SiteStatsFingerprint(
fingerprint,
stats=site_stats
)
df = site_stats_fingerprint.featurize_dataframe(
df,
"Input data|structure",
multiindex=False,
ignore_errors=True
)
fingerprint_name = fingerprint.__class__.__name__
# rename some features for backwards compatibility with pretrained models
if fingerprint_name == "GeneralizedRadialDistributionFunction":
fingerprint_name = "GeneralizedRDF"
elif fingerprint_name == "AGNIFingerprints":
fingerprint_name = "AGNIFingerPrint"
elif fingerprint_name == "BondOrientationalParameter":
fingerprint_name = "BondOrientationParameter"
elif fingerprint_name == "GaussianSymmFunc":
fingerprint_name = "ChemEnvSiteFingerprint|GaussianSymmFunc"
if "|" not in fingerprint_name:
fingerprint_name += "|"
df.columns = [f"{fingerprint_name}{x}" if "|" not in x else x for x in df.columns]
df = df.loc[:, (df != 0).any(axis=0)]
return clean_df(df)