# Combined SISSO Feature Expressions for some Datasets using RUNG 2 and 4 features
# Each section below originates from a different dataset file.

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# Dataset: matbench_dielectric
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# Translated Feature Expressions
# Format: Feature_Number: Python Expression
#--------------------------------------------------
Feature 1: safe_div((abs(df["CrystalNNFingerprint|mean q2 CN_12"] - df["Miedema|Miedema_deltaH_inter"])), (abs(df["XRDPowderPattern|xrd_46"] - df["AtomicPackingEfficiency|mean simul. packing efficiency"])))
Feature 2: safe_div((df["AGNIFingerPrint|std_dev AGNI eta=8.00e-01"] * df["Miedema|Miedema_deltaH_ss_min"]), (abs(df["StructuralHeterogeneity|avg_dev neighbor distance variation"] - df["ValenceOrbital|frac p valence electrons"])))
Feature 3: safe_div(safe_div(df["Miedema|Miedema_deltaH_ss_min"], df["XRDPowderPattern|xrd_40"]), (df["XRDPowderPattern|xrd_46"] - df["AtomicPackingEfficiency|mean simul. packing efficiency"]))
Feature 4: safe_div(safe_div(df["Miedema|Miedema_deltaH_ss_min"], df["ValenceOrbital|frac p valence electrons"]), (df["DensityFeatures|vpa"] + df["ElementProperty|MagpieData maximum Electronegativity"]))
Feature 5: safe_div((df["TMetalFraction|transition metal fraction"] * df["Miedema|Miedema_deltaH_ss_min"]), (abs(df["XRDPowderPattern|xrd_46"] - df["AtomicPackingEfficiency|mean simul. packing efficiency"])))
Feature 6: safe_div((df["Miedema|Miedema_deltaH_ss_min"] * df["AtomicOrbitals|HOMO_character"]), (abs(df["CoulombMatrix|coulomb matrix eig 7"] - df["BandCenter|band center"])))
Feature 7: safe_div(safe_div(df["Miedema|Miedema_deltaH_ss_min"], df["XRDPowderPattern|xrd_85"]), (df["XRDPowderPattern|xrd_46"] - df["AtomicPackingEfficiency|mean simul. packing efficiency"]))
Feature 8: safe_div(safe_div(df["ElementProperty|MagpieData maximum Electronegativity"], df["XRDPowderPattern|xrd_46"]), (abs(df["ElementFraction|Ni"] - df["BandCenter|band center"])))
Feature 9: safe_div(safe_div(df["ElementProperty|MagpieData maximum Electronegativity"], df["XRDPowderPattern|xrd_46"]), (df["ElementFraction|Ni"] - df["BandCenter|band center"]))
Feature 10: safe_div((df["MaximumPackingEfficiency|max packing efficiency"] * df["Miedema|Miedema_deltaH_ss_min"]), (abs(df["ElementFraction|Ni"] - df["BandCenter|band center"])))
Feature 11: safe_div((df["Miedema|Miedema_deltaH_ss_min"] * df["AtomicOrbitals|HOMO_character"]), (df["XRDPowderPattern|xrd_40"] * df["ValenceOrbital|frac p valence electrons"]))
Feature 12: safe_div((df["MaximumPackingEfficiency|max packing efficiency"] * df["Miedema|Miedema_deltaH_ss_min"]), (df["ElementFraction|Ni"] - df["BandCenter|band center"]))
Feature 13: (df["CrystalNNFingerprint|mean q6 CN_11"] * df["ElementFraction|Ni"]) - safe_div(df["Miedema|Miedema_deltaH_ss_min"], df["XRDPowderPattern|xrd_46"])
Feature 14: abs((df["CrystalNNFingerprint|mean q6 CN_11"] * df["ElementFraction|Ni"]) - safe_div(df["Miedema|Miedema_deltaH_ss_min"], df["XRDPowderPattern|xrd_46"]))
Feature 15: safe_div((df["TMetalFraction|transition metal fraction"] * df["Miedema|Miedema_deltaH_inter"]), (abs(df["XRDPowderPattern|xrd_46"] - df["AtomicPackingEfficiency|mean simul. packing efficiency"])))
Feature 16: abs((df["ChemEnvSiteFingerprint|std_dev PP:6"] * df["ElementProperty|MagpieData mode MeltingT"]) - (df["DensityFeatures|vpa"] * df["ElementFraction|Os"]))
Feature 17: safe_div((df["AGNIFingerPrint|std_dev AGNI eta=8.00e-01"] * df["ElementProperty|MagpieData mode MeltingT"]), (df["ChemEnvSiteFingerprint|std_dev PP:6"] + df["AGNIFingerPrint|std_dev AGNI eta=8.00e-01"]))
Feature 18: (df["DensityFeatures|density"] - df["ElementProperty|MagpieData maximum Electronegativity"]) - safe_div(df["ElementFraction|Ni"], df["DensityFeatures|density"])
Feature 19: (df["DensityFeatures|density"] - df["ElementProperty|MagpieData maximum Electronegativity"]) + safe_div(df["ElementFraction|Os"], df["XRDPowderPattern|xrd_85"])
Feature 20: (df["DensityFeatures|density"] - df["ElementProperty|MagpieData maximum Electronegativity"]) + safe_div(df["ElementFraction|Ni"], df["OPSiteFingerprint|mean square co-planar CN_4"])
Feature 21: (df["DensityFeatures|density"] - df["ElementProperty|MagpieData maximum Electronegativity"]) + safe_div(df["ElementFraction|Te"], df["BandCenter|band center"])
Feature 22: (df["DensityFeatures|density"] - df["ElementProperty|MagpieData maximum Electronegativity"]) - safe_div(df["ElementFraction|Os"], df["ValenceOrbital|frac p valence electrons"])
Feature 23: safe_div((df["CoulombMatrix|coulomb matrix eig 7"] * df["ElementFraction|Ni"]), (df["DensityFeatures|vpa"] - df["ElementProperty|MagpieData mode MeltingT"]))
Feature 24: (df["OPSiteFingerprint|mean square co-planar CN_4"] + df["XRDPowderPattern|xrd_40"]) * (df["ElementProperty|MagpieData mean GSmagmom"] * df["ElementFraction|Ni"])
Feature 25: (df["CoulombMatrix|coulomb matrix eig 7"] * df["ElementProperty|MagpieData maximum Electronegativity"]) * safe_div(df["ElementFraction|Ni"], df["StructuralHeterogeneity|avg_dev neighbor distance variation"])
Feature 26: (df["MaximumPackingEfficiency|max packing efficiency"] + df["StructuralHeterogeneity|avg_dev neighbor distance variation"]) * (df["ElementProperty|MagpieData mode MeltingT"] * df["ElementFraction|Ni"])
Feature 27: (abs(df["CrystalNNFingerprint|mean q6 CN_11"] - df["ElementFraction|Ni"])) - (abs(df["CrystalNNFingerprint|mean q6 CN_11"]))
Feature 28: (df["CrystalNNFingerprint|mean q6 CN_11"] - df["ChemEnvSiteFingerprint|mean PP:6"]) - (abs(df["CrystalNNFingerprint|mean q6 CN_11"] - df["ElementFraction|Ni"]))
Feature 29: (abs(df["CrystalNNFingerprint|mean q6 CN_11"] - df["ElementFraction|Os"])) - (abs(df["CrystalNNFingerprint|mean q6 CN_11"] - df["ElementFraction|Ni"]))
Feature 30: safe_div((df["ElementFraction|Os"] - df["ElementFraction|Ni"]), (abs(df["CrystalNNFingerprint|mean q6 CN_11"] - df["XRDPowderPattern|xrd_46"])))
Feature 31: safe_div((df["ElementFraction|Os"] + df["ElementFraction|Ni"]), (df["CrystalNNFingerprint|mean q6 CN_11"] + df["XRDPowderPattern|xrd_46"]))
Feature 32: safe_div((df["ElementFraction|Os"] + df["ElementFraction|Ni"]), (df["CrystalNNFingerprint|mean q6 CN_11"] - df["XRDPowderPattern|xrd_46"]))
Feature 33: (df["OPSiteFingerprint|mean cuboctahedral CN_12"] - df["XRDPowderPattern|xrd_40"]) * (df["CoulombMatrix|coulomb matrix eig 7"] * df["ElementFraction|Ni"])
Feature 34: safe_div((df["ElementFraction|Os"] - df["ElementFraction|Ni"]), (abs(df["DensityFeatures|vpa"] - df["TMetalFraction|transition metal fraction"])))
Feature 35: (df["XRDPowderPattern|xrd_40"] * df["ElementFraction|Ni"]) - safe_div(df["ElementFraction|Os"], df["DensityFeatures|density"])

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# Dataset: matbench_steels
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# Translated Feature Expressions
# Format: Feature_Number: Python Expression
#--------------------------------------------------
Feature 1: safe_div((df["ElementFraction|Mn"] * df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]), (df["ElementProperty|MagpieData mean NsValence"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]))
Feature 2: (df["ElementProperty|MagpieData avg_dev AtomicWeight"] + df["ElementFraction|Fe"]) * (df["ElementFraction|Ti"] * df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"])
Feature 3: safe_div(safe_div(df["AtomicPackingEfficiency|mean simul. packing efficiency"], df["Stoichiometry|2-norm"]), (abs(df["ElementFraction|Mn"] - df["ElementFraction|C"])))
Feature 4: safe_div(safe_div(df["ElementFraction|Mn"], df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]), (df["ElementProperty|MagpieData mean NsValence"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]))
Feature 5: (df["Stoichiometry|2-norm"] - df["ElementFraction|Fe"]) * (df["ElementFraction|Co"] * df["ElementFraction|Ti"])
Feature 6: safe_div(safe_div(df["ElementFraction|Ni"], df["Stoichiometry|2-norm"]), (abs(df["ElementFraction|Mn"] - df["ElementFraction|C"])))
Feature 7: safe_div((df["ElementFraction|Cr"] + df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"]), (abs(df["ElementProperty|MagpieData mean NsValence"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]))
Feature 8: safe_div((df["ElementFraction|V"] - df["ElementFraction|Ti"]), (df["Stoichiometry|2-norm"] * df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]))
Feature 9: safe_div((df["ElementFraction|Co"] + df["ElementFraction|Si"]), (abs(df["ElementFraction|Mn"] - df["ElementFraction|C"])))
Feature 10: safe_div((df["ElementFraction|Co"] - df["ElementFraction|Cr"]), (df["ElementProperty|MagpieData mean NsValence"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]))
Feature 11: (df["Stoichiometry|2-norm"] + df["ElementProperty|MagpieData avg_dev AtomicWeight"]) * (df["ElementFraction|Co"] * df["ElementFraction|Ti"])
Feature 12: safe_div((abs(df["ElementFraction|Co"] - df["ElementFraction|Si"])), (abs(df["ElementFraction|Mn"] - df["ElementFraction|C"])))
Feature 13: safe_div((df["ElementProperty|MagpieData mean NdValence"] + df["ElementProperty|MagpieData avg_dev Row"]), (df["ElementProperty|MagpieData mean NsValence"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]))
Feature 14: safe_div(safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"]), (df["Miedema|Miedema_deltaH_inter"] + df["ElementProperty|MagpieData mean MeltingT"]))
Feature 15: safe_div(safe_div(df["Stoichiometry|2-norm"], df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]), (df["ElementProperty|MagpieData mean NsValence"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]))
Feature 16: abs((df["Miedema|Miedema_deltaH_inter"] - df["ElementProperty|MagpieData mean CovalentRadius"]) - safe_div(df["AtomicPackingEfficiency|mean simul. packing efficiency"], df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 17: abs((df["Miedema|Miedema_deltaH_inter"] + df["ElementProperty|MagpieData avg_dev NUnfilled"]) - safe_div(df["AtomicPackingEfficiency|mean simul. packing efficiency"], df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 18: abs((df["Miedema|Miedema_deltaH_inter"] - df["BandCenter|band center"]) - safe_div(df["AtomicPackingEfficiency|mean simul. packing efficiency"], df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 19: (df["Miedema|Miedema_deltaH_inter"] * df["ElementFraction|Mo"]) * (df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"] - df["AtomicPackingEfficiency|mean abs simul. packing efficiency"])
Feature 20: abs((df["Miedema|Miedema_deltaH_inter"] + df["ElementProperty|MagpieData mean GSvolume_pa"]) - safe_div(df["AtomicPackingEfficiency|mean simul. packing efficiency"], df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 21: abs((df["YangSolidSolution|Yang delta"] + df["Miedema|Miedema_deltaH_inter"]) - safe_div(df["AtomicPackingEfficiency|mean simul. packing efficiency"], df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 22: abs((df["Miedema|Miedema_deltaH_inter"] + df["ElementProperty|MagpieData avg_dev NdValence"]) - safe_div(df["AtomicPackingEfficiency|mean simul. packing efficiency"], df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 23: abs((df["ElementProperty|MagpieData mean NdValence"] + df["ElementProperty|MagpieData avg_dev Electronegativity"]) - safe_div(df["ElementFraction|Co"], df["ElementFraction|Fe"]))
Feature 24: (df["Miedema|Miedema_deltaH_inter"] * df["ElementFraction|Co"]) * (df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"] - df["AtomicPackingEfficiency|mean abs simul. packing efficiency"])
Feature 25: abs((df["ValenceOrbital|frac s valence electrons"] * df["AtomicPackingEfficiency|mean simul. packing efficiency"]) - safe_div(df["Miedema|Miedema_deltaH_ss_min"], df["Stoichiometry|2-norm"]))
Feature 26: (abs(df["ElementFraction|Co"] - df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"])) * (abs(df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"] - df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]))
Feature 27: (df["ElementFraction|Ni"] * df["ElementFraction|Co"]) * (abs(df["ElementFraction|Cr"] - df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]))
Feature 28: abs((df["ElementProperty|MagpieData mean GSbandgap"] * df["ElementProperty|MagpieData avg_dev MeltingT"]) - safe_div(df["ElementFraction|Ni"], df["Stoichiometry|2-norm"]))
Feature 29: (df["Miedema|Miedema_deltaH_ss_min"] * df["ElementFraction|Mo"]) * (df["ElementProperty|MagpieData avg_dev NdUnfilled"] - df["AtomicPackingEfficiency|mean abs simul. packing efficiency"])
Feature 30: abs((df["Stoichiometry|2-norm"] * df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"]) - (df["ElementFraction|Ni"] - df["ElementFraction|Co"]))
Feature 31: (abs(df["Miedema|Miedema_deltaH_inter"] - df["ElementFraction|Nb"])) + safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ValenceOrbital|frac s valence electrons"])
Feature 32: (abs(df["Miedema|Miedema_deltaH_inter"] - df["ElementFraction|C"])) + safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ValenceOrbital|frac s valence electrons"])
Feature 33: (abs(df["Miedema|Miedema_deltaH_inter"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"])) + safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ValenceOrbital|frac s valence electrons"])
Feature 34: (abs(df["Miedema|Miedema_deltaH_inter"] - df["ElementFraction|Al"])) + safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ValenceOrbital|frac s valence electrons"])
Feature 35: abs((df["YangSolidSolution|Yang omega"] - df["ElementProperty|MagpieData avg_dev NUnfilled"]) - (abs(df["ElementFraction|Ni"] - df["ElementFraction|Cr"])))
Feature 36: (abs(df["Miedema|Miedema_deltaH_inter"] - df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"])) - safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ElementProperty|MagpieData mean Number"])
Feature 37: (abs(df["Miedema|Miedema_deltaH_inter"] - df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"])) - safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ElementProperty|MagpieData mean Number"])
Feature 38: (abs(df["Miedema|Miedema_deltaH_inter"] - df["ElementFraction|Cr"])) - safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ElementProperty|MagpieData mean Number"])
Feature 39: (abs(df["Miedema|Miedema_deltaH_inter"] - df["ElementFraction|Cr"])) + safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ValenceOrbital|frac s valence electrons"])
Feature 40: safe_div((abs(df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"] - df["AtomicPackingEfficiency|mean abs simul. packing efficiency"])), (df["ElementProperty|MagpieData avg_dev NUnfilled"] - df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]))
Feature 41: (abs(df["Miedema|Miedema_deltaH_inter"] - df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"])) + safe_div(df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"], df["ValenceOrbital|frac s valence electrons"])
Feature 42: safe_div((df["Miedema|Miedema_deltaH_inter"] * df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"]), (df["ElementProperty|MagpieData mean NsValence"] + df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"]))
Feature 43: safe_div(safe_div(df["ElementFraction|Mo"], df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]), (df["YangSolidSolution|Yang delta"] - df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 44: abs((df["ElementProperty|MagpieData avg_dev NdValence"] + df["ElementProperty|MagpieData mean NdValence"]) - (df["AtomicPackingEfficiency|dist from 1 clusters |APE| < 0.010"] - df["AtomicPackingEfficiency|mean simul. packing efficiency"]))
Feature 45: safe_div((df["Miedema|Miedema_deltaH_amor"] / df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]), (abs(df["ElementFraction|Ti"] - df["ElementFraction|C"])))
Feature 46: safe_div((df["ElementProperty|MagpieData mean NdUnfilled"] * df["ElementFraction|Mo"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 47: safe_div(safe_div(df["ElementFraction|Mo"], df["ElementProperty|MagpieData avg_dev AtomicWeight"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 48: safe_div((df["YangSolidSolution|Yang delta"] * df["ElementFraction|Mo"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 49: safe_div((df["ElementFraction|Mo"] * df["ElementFraction|Fe"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 50: safe_div((df["ElementProperty|MagpieData range MendeleevNumber"] * df["ElementFraction|Mo"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 51: safe_div((df["ElementFraction|Mo"] - df["ElementFraction|Nb"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 52: safe_div((df["ElementFraction|Mo"] + df["ElementFraction|Mn"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 53: safe_div((df["ElementProperty|MagpieData mean CovalentRadius"] * df["ElementFraction|Mo"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 54: safe_div((df["ElementProperty|MagpieData mean Number"] * df["ElementFraction|Mo"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 55: safe_div((df["ElementProperty|MagpieData mean AtomicWeight"] * df["ElementFraction|Mo"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 56: safe_div((df["ElementProperty|MagpieData mean GSbandgap"] * df["ElementFraction|Mo"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 57: safe_div((df["ElementFraction|Mo"] + df["ElementFraction|V"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 58: safe_div((df["ElementFraction|Mo"] * df["ElementFraction|Ni"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 59: safe_div((df["ElementFraction|Mo"] + df["ElementFraction|Si"]), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 60: safe_div((abs(df["ElementFraction|Mo"] - df["ElementFraction|Si"])), (df["ElementProperty|MagpieData mean GSbandgap"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 61: abs((df["ElementProperty|MagpieData mean NdValence"] + df["ElementProperty|MagpieData avg_dev Row"]) - (df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"] * df["AtomicPackingEfficiency|mean simul. packing efficiency"]))
Feature 62: abs(abs(df["ElementFraction|Mo"] - df["ElementFraction|Al"]) - safe_div(df["ElementFraction|C"], df["BandCenter|band center"]))
Feature 63: safe_div((df["ElementFraction|Ti"] * df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"]), (df["ElementProperty|MagpieData mean MendeleevNumber"] + df["ElementFraction|Fe"]))
Feature 64: safe_div(safe_div(df["ElementFraction|Ni"], df["ElementProperty|MagpieData mean NsValence"]), (df["ElementFraction|Cr"] + df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"]))
Feature 65: safe_div(safe_div(df["ElementFraction|Cr"], df["ElementFraction|Mn"]), (df["ElementProperty|MagpieData avg_dev NdValence"] + df["BandCenter|band center"]))
Feature 66: abs((df["ElementProperty|MagpieData mean MeltingT"] * df["ElementFraction|C"]) - (df["ElementFraction|Mo"] - df["ElementFraction|Al"]))  # no division inside the abs outer
Feature 67: abs((df["ElementProperty|MagpieData mean NdUnfilled"] * df["ElementFraction|C"]) - (df["ElementFraction|Mo"] - df["ElementFraction|Al"]))  # no division
Feature 68: safe_div((df["ElementFraction|Ti"] * df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]), (df["ElementProperty|MagpieData mean MendeleevNumber"] + df["ElementFraction|Fe"]))
Feature 69: abs(abs(df["ElementFraction|Mo"] - df["ElementFraction|Al"]) - safe_div(df["ElementFraction|C"], df["ElementProperty|MagpieData mean CovalentRadius"]))
Feature 70: safe_div((df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"] * df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"]), (df["ElementProperty|MagpieData mean NsValence"] + df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]))
Feature 71: safe_div((df["ElementFraction|Al"] - df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"]), (df["ElementProperty|MagpieData mean NsValence"] + df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]))
Feature 72: safe_div((df["ElementFraction|Cr"] + df["AtomicPackingEfficiency|dist from 5 clusters |APE| < 0.010"]), (abs(df["ElementProperty|MagpieData mean NpUnfilled"] - df["ElementProperty|MagpieData avg_dev CovalentRadius"])))
Feature 73: safe_div(safe_div(df["ElementFraction|Ti"], df["ElementProperty|MagpieData mean NsValence"]), (df["ElementProperty|MagpieData mean NdValence"] + df["ElementProperty|MagpieData avg_dev Row"]))
Feature 74: safe_div(safe_div(df["ElementFraction|Ti"], df["ElementProperty|MagpieData mean NsValence"]), (df["ElementProperty|MagpieData mean GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev NdValence"]))
Feature 75: safe_div((df["ElementFraction|Ti"] - df["AtomicPackingEfficiency|dist from 3 clusters |APE| < 0.010"]), (df["ElementProperty|MagpieData mean NsValence"] + df["AtomicPackingEfficiency|mean abs simul. packing efficiency"]))

############################
# Dataset: matbench_glass
############################

# Translated Feature Expressions
# Format: Feature_Number: Python Expression
#--------------------------------------------------
Feature 1: (df["Miedema|Miedema_deltaH_inter"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (df["ElementProperty|MagpieData mode GSvolume_pa"] * df["ElementProperty|MagpieData mean Row"])
Feature 2: (df["Miedema|Miedema_deltaH_inter"] * df["ElementProperty|MagpieData mean Row"]) * (df["ElementProperty|MagpieData mode GSvolume_pa"] * df["ElementProperty|MagpieData mean NUnfilled"])
Feature 3: (df["Miedema|Miedema_deltaH_inter"] * df["ElementProperty|MagpieData mode GSvolume_pa"]) * (df["ElementProperty|MagpieData mean NUnfilled"] * df["ElementProperty|MagpieData mean Row"])
Feature 4: (df["Miedema|Miedema_deltaH_ss_min"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (abs(df["ElementProperty|MagpieData mean Row"] - df["ElementProperty|MagpieData range MeltingT"]))
Feature 5: (abs(df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData maximum NdValence"])) * exp(df["ElementProperty|MagpieData mean NValence"])
Feature 6: (df["Miedema|Miedema_deltaH_amor"] * df["ElementProperty|MagpieData mean Row"]) * (df["ElementProperty|MagpieData range GSvolume_pa"] + df["ElementProperty|MagpieData mean NUnfilled"])
Feature 7: (df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]) + (df["ElementProperty|MagpieData mean NValence"] + df["ElementProperty|MagpieData avg_dev CovalentRadius"])
Feature 8: (abs(df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"])) + (df["ElementProperty|MagpieData mean NValence"] + df["ElementProperty|MagpieData avg_dev CovalentRadius"])
Feature 9: (df["Miedema|Miedema_deltaH_inter"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (df["ElementProperty|MagpieData mean NValence"] + df["ElementProperty|MagpieData mean CovalentRadius"])
Feature 10: (df["ElementProperty|MagpieData mean SpaceGroupNumber"] + df["ElementProperty|MagpieData mean Row"]) - (abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"]))
Feature 11: (df["YangSolidSolution|Yang delta"] * df["ElementProperty|MagpieData avg_dev AtomicWeight"]) - (abs(df["Miedema|Miedema_deltaH_ss_min"] - df["ElementProperty|MagpieData mean Row"]))
Feature 12: (df["Miedema|Miedema_deltaH_ss_min"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (df["ElementProperty|MagpieData mean Row"] - df["ElementProperty|MagpieData range MeltingT"])
Feature 13: (abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"])) - (abs(df["ElementProperty|MagpieData mean NsUnfilled"] - df["ElementProperty|MagpieData mean Row"]))
Feature 14: (abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"])) + (df["ElementProperty|MagpieData mean NsUnfilled"] - df["ElementProperty|MagpieData mean Row"])
Feature 15: (abs(df["Miedema|Miedema_deltaH_ss_min"] - df["ElementProperty|MagpieData avg_dev Row"])) + (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData avg_dev NpValence"])
Feature 16: (df["Miedema|Miedema_deltaH_ss_min"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData mean NdValence"])
Feature 17: safe_div((df["ElementProperty|MagpieData mean Row"]**2), (df["YangSolidSolution|Yang delta"] + df["ElementProperty|MagpieData range NUnfilled"]))
Feature 18: abs((df["ValenceOrbital|frac p valence electrons"] * df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"]) - (df["ElementProperty|MagpieData mean NUnfilled"] * df["ElementProperty|MagpieData mean Row"]))
Feature 19: (df["Miedema|Miedema_deltaH_ss_min"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (abs(df["ElementProperty|MagpieData mean Row"] - df["ElementProperty|MagpieData range MeltingT"]))
Feature 20: safe_div((df["YangSolidSolution|Yang delta"] / df["ElementProperty|MagpieData avg_dev NpValence"]), (df["ElementProperty|MagpieData mean Row"] - df["ElementFraction|Mo"]))
Feature 21: safe_div((df["YangSolidSolution|Yang delta"] * df["ElementProperty|MagpieData mean Row"]), (df["ElementProperty|MagpieData avg_dev NpValence"] - df["ElementFraction|Mo"]))
Feature 22: safe_div((df["ElementProperty|MagpieData mean Row"] * df["ElementProperty|MagpieData mean MeltingT"]), (df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"]))
Feature 23: safe_div((df["ElementProperty|MagpieData mean Row"] * df["ElementProperty|MagpieData range MendeleevNumber"]), (df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"]))
Feature 24: (abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"])) + (df["ElementProperty|MagpieData maximum GSvolume_pa"] * df["ElementProperty|MagpieData mean Row"])
Feature 25: safe_div((df["ElementProperty|MagpieData maximum GSvolume_pa"] * df["ElementProperty|MagpieData mean Row"]), (df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"]))
Feature 26: safe_div((df["YangSolidSolution|Yang delta"] * df["ElementProperty|MagpieData mean Row"]), (abs(df["ElementProperty|MagpieData avg_dev NpValence"] - df["ElementFraction|Mo"])))
Feature 27: (df["Miedema|Miedema_deltaH_inter"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (df["ElementProperty|MagpieData mean NValence"] + df["ElementProperty|MagpieData mean CovalentRadius"])
Feature 28: safe_div((df["ElementProperty|MagpieData maximum GSvolume_pa"] * df["ElementProperty|MagpieData mean Row"]), (abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"])))
Feature 29: (df["Miedema|Miedema_deltaH_ss_min"] * df["ElementProperty|MagpieData mean NUnfilled"]) * (df["ElementProperty|MagpieData mean Row"] - df["ElementProperty|MagpieData range MeltingT"])
Feature 30: (df["ElementProperty|MagpieData range NUnfilled"] + df["ElementProperty|MagpieData mean NpValence"]) * (df["ElementProperty|MagpieData range NUnfilled"] + df["ElementProperty|MagpieData mean Row"])
Feature 31: (abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData mean NsUnfilled"])) - (df["ElementProperty|MagpieData mean NdValence"] * df["ElementProperty|MagpieData mean CovalentRadius"])
Feature 32: abs((df["ValenceOrbital|frac d valence electrons"] + df["ElementProperty|MagpieData maximum GSvolume_pa"]) - (df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] - df["ElementFraction|Cu"]))
Feature 33: abs((df["ValenceOrbital|frac d valence electrons"] + df["ElementProperty|MagpieData maximum GSvolume_pa"]) - (df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] - df["ElementFraction|Ce"]))
Feature 34: (abs(df["YangSolidSolution|Yang delta"] - df["ElementFraction|Ti"])) + (df["Stoichiometry|2-norm"] + df["ElementFraction|Cu"])
Feature 35: (df["Stoichiometry|2-norm"] + df["ElementFraction|Cu"]) - (df["ElementProperty|MagpieData mode GSvolume_pa"] * df["ElementProperty|MagpieData range GSvolume_pa"])
Feature 36: (df["Stoichiometry|2-norm"] + df["ElementProperty|MagpieData avg_dev MendeleevNumber"]) + (df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] * df["ElementProperty|MagpieData avg_dev CovalentRadius"])
Feature 37: (abs(df["Miedema|Miedema_deltaH_ss_min"] - df["ElementProperty|MagpieData mean Row"])) - (df["ElementProperty|MagpieData avg_dev GSvolume_pa"] * df["ElementProperty|MagpieData avg_dev CovalentRadius"])
Feature 38: abs((df["ValenceOrbital|frac d valence electrons"] + df["ElementProperty|MagpieData maximum GSvolume_pa"]) - (df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] - df["ElementFraction|Ti"]))
Feature 39: abs((df["Stoichiometry|2-norm"] - df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"]) - (df["ElementProperty|MagpieData avg_dev CovalentRadius"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]))
Feature 40: (df["YangSolidSolution|Yang delta"] - df["Stoichiometry|2-norm"]) - safe_div(df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"], df["ElementProperty|MagpieData avg_dev NpValence"])
Feature 41: safe_div((df["ElementProperty|MagpieData mean GSmagmom"] * df["ElementProperty|MagpieData mode GSvolume_pa"]), (df["ElementProperty|MagpieData maximum GSvolume_pa"] + df["ElementProperty|MagpieData mean Row"]))
Feature 42: safe_div(safe_div(df["ElementProperty|MagpieData avg_dev Row"], df["ElementProperty|MagpieData range GSvolume_pa"]), (df["TMetalFraction|transition metal fraction"] + df["ElementProperty|MagpieData avg_dev CovalentRadius"]))
Feature 43: (df["ElementProperty|MagpieData avg_dev GSvolume_pa"] * df["ElementProperty|MagpieData avg_dev CovalentRadius"]) + (abs(df["ElementProperty|MagpieData avg_dev NpValence"] - df["ElementProperty|MagpieData mean Column"]))
Feature 44: abs((df["ValenceOrbital|frac d valence electrons"] + df["ElementProperty|MagpieData avg_dev Electronegativity"]) - (df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] * df["ElementProperty|MagpieData maximum GSvolume_pa"]))
Feature 45: abs((df["ElementProperty|MagpieData avg_dev GSvolume_pa"] + df["ElementFraction|Ce"]) - (df["ElementProperty|MagpieData avg_dev NpValence"] - df["ElementProperty|MagpieData mean Column"]))
Feature 46: safe_div((abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData maximum Column"])), (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData range NUnfilled"]))
Feature 47: (abs(df["Miedema|Miedema_deltaH_inter"] - df["ElementProperty|MagpieData maximum Column"])) * (abs(df["ElementProperty|MagpieData maximum GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev NpValence"]))
Feature 48: safe_div((df["ElementProperty|MagpieData maximum Column"] - df["ElementProperty|MagpieData range MendeleevNumber"]), (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData range NUnfilled"]))
Feature 49: safe_div((abs(df["ElementProperty|MagpieData maximum Column"] - df["ElementProperty|MagpieData range MendeleevNumber"])), (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData range NUnfilled"]))
Feature 50: safe_div((abs(df["IonProperty|max ionic char"] - df["ElementProperty|MagpieData maximum Column"])), (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData range NUnfilled"]))
Feature 51: safe_div((df["ElementProperty|MagpieData range Electronegativity"] - df["ElementProperty|MagpieData maximum Column"]), (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData range NUnfilled"]))
Feature 52: safe_div((df["ElementProperty|MagpieData range GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev AtomicWeight"]), (abs(df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData range NUnfilled"])))
Feature 53: abs((df["ElementProperty|MagpieData mode GSvolume_pa"] * df["ElementProperty|MagpieData maximum Column"]) - safe_div(df["ElementProperty|MagpieData maximum GSvolume_pa"], df["ElementProperty|MagpieData mean NdUnfilled"]))
Feature 54: (df["Miedema|Miedema_deltaH_inter"] * df["ElementProperty|MagpieData maximum Column"]) * (df["ElementProperty|MagpieData avg_dev NUnfilled"] + df["ElementProperty|MagpieData avg_dev NpValence"])
Feature 55: (abs(df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData range NUnfilled"])) - (abs(df["ElementProperty|MagpieData avg_dev GSvolume_pa"] - df["ElementProperty|MagpieData range GSvolume_pa"]))
Feature 56: safe_div((abs(df["ElementProperty|MagpieData range Electronegativity"] - df["ElementProperty|MagpieData maximum Column"])), (df["ElementProperty|MagpieData mode GSvolume_pa"] + df["ElementProperty|MagpieData range NUnfilled"]))
Feature 57: abs(df["TMetalFraction|transition metal fraction"] - df["ElementProperty|MagpieData mode GSvolume_pa"]) - (df["ElementProperty|MagpieData range NUnfilled"] + df["ElementProperty|MagpieData mean NpValence"])
Feature 58: (abs(df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData range NUnfilled"])) - (df["ElementProperty|MagpieData range GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev CovalentRadius"])
Feature 59: (abs(df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData range NUnfilled"])) - (abs(df["ElementProperty|MagpieData range GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev CovalentRadius"]))
Feature 60: (abs(df["ElementProperty|MagpieData mode GSvolume_pa"] - df["ElementProperty|MagpieData range NUnfilled"])) - (df["ElementProperty|MagpieData range GSvolume_pa"] - df["ElementProperty|MagpieData avg_dev AtomicWeight"])
Feature 61: (df["ValenceOrbital|frac d valence electrons"] * df["ElementProperty|MagpieData avg_dev NdUnfilled"]) - safe_div(df["ElementProperty|MagpieData avg_dev NUnfilled"], df["ElementProperty|MagpieData mean SpaceGroupNumber"])
Feature 62: safe_div((df["ElementProperty|MagpieData avg_dev NUnfilled"] + df["ElementFraction|Ti"]), (df["ElementProperty|MagpieData mean CovalentRadius"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]))
Feature 63: safe_div((df["ElementProperty|MagpieData avg_dev NUnfilled"] + df["ElementFraction|Cu"]), (df["ElementProperty|MagpieData mean CovalentRadius"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]))
Feature 64: (abs(df["ElementProperty|MagpieData mean SpaceGroupNumber"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"])) + exp(df["ElementProperty|MagpieData avg_dev NUnfilled"])
Feature 65: (df["ElementProperty|MagpieData avg_dev NUnfilled"] * df["ElementProperty|MagpieData avg_dev MendeleevNumber"]) + safe_div(df["ElementProperty|MagpieData range MendeleevNumber"], df["ElementProperty|MagpieData range GSvolume_pa"])
Feature 66: (df["ElementProperty|MagpieData mean SpaceGroupNumber"] * df["ElementProperty|MagpieData avg_dev NUnfilled"]) + (abs(df["ElementProperty|MagpieData mean SpaceGroupNumber"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"]))
Feature 67: (df["ElementProperty|MagpieData mode GSvolume_pa"] * df["ElementProperty|MagpieData avg_dev GSvolume_pa"]) - (df["ElementProperty|MagpieData avg_dev NUnfilled"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"])
Feature 68: (df["ElementProperty|MagpieData range GSvolume_pa"] + df["ElementProperty|MagpieData mean NValence"]) - (df["ElementProperty|MagpieData avg_dev NUnfilled"] - df["ElementProperty|MagpieData avg_dev NdUnfilled"])
Feature 69: safe_div((df["ElementProperty|MagpieData avg_dev NUnfilled"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]), (df["ElementProperty|MagpieData mean NdUnfilled"] - df["ElementProperty|MagpieData mean NpValence"]))
Feature 70: (df["ElementProperty|MagpieData avg_dev NUnfilled"] + df["ElementProperty|MagpieData mean NpValence"]) * exp(df["ElementProperty|MagpieData avg_dev MendeleevNumber"])
Feature 71: exp(df["ElementProperty|MagpieData avg_dev NUnfilled"]) + safe_div(df["ElementProperty|MagpieData avg_dev MendeleevNumber"], df["ElementProperty|MagpieData mean NpValence"])
Feature 72: abs(abs(df["ElementProperty|MagpieData avg_dev GSvolume_pa"] - df["ElementProperty|MagpieData maximum NdValence"]) - safe_div(df["ElementProperty|MagpieData range MendeleevNumber"], df["ElementProperty|MagpieData range GSvolume_pa"]))
Feature 73: (df["ElementProperty|MagpieData avg_dev NUnfilled"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]) - (df["ElementProperty|MagpieData avg_dev NdUnfilled"] * df["ElementProperty|MagpieData mean NdValence"])
Feature 74: safe_div((df["ElementProperty|MagpieData avg_dev NUnfilled"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]), (df["ElementProperty|MagpieData mean NdUnfilled"] - df["ElementProperty|MagpieData avg_dev NpValence"]))
Feature 75: safe_div((df["ElementProperty|MagpieData avg_dev NUnfilled"] - df["ElementProperty|MagpieData avg_dev MendeleevNumber"]), (abs(df["ElementProperty|MagpieData mean NdUnfilled"] - df["ElementProperty|MagpieData avg_dev NpValence"])))

############################
# Dataset: matbench_expt_gap
############################

# Translated Feature Expressions
# Format: Feature_Number: Python Expression
#--------------------------------------------------
Feature 1: (df["ValenceOrbital|frac p valence electrons"] * df["TMetalFraction|transition metal fraction"]) + (df["TMetalFraction|transition metal fraction"] - df["ElementProperty|MagpieData mean NpValence"])
Feature 2: (df["TMetalFraction|transition metal fraction"] + df["AtomicOrbitals|HOMO_element"]) - (df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] - df["AtomicOrbitals|HOMO_energy"])
Feature 3: (abs(df["TMetalFraction|transition metal fraction"] - df["Stoichiometry|0-norm"])) - (df["TMetalFraction|transition metal fraction"] + df["AtomicOrbitals|HOMO_energy"])
Feature 4: (abs(df["TMetalFraction|transition metal fraction"] - df["Stoichiometry|0-norm"])) - (df["TMetalFraction|transition metal fraction"] - df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"])
Feature 5: (df["TMetalFraction|transition metal fraction"] - df["ElementProperty|MagpieData mean NpValence"]) + (df["AtomicOrbitals|HOMO_energy"] + df["AtomicOrbitals|HOMO_element"])
Feature 6: (df["ElementProperty|MagpieData mean NpValence"] - df["AtomicOrbitals|HOMO_energy"]) * (abs(df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_energy"]))
Feature 7: (abs(df["ValenceOrbital|frac p valence electrons"] - df["TMetalFraction|transition metal fraction"])) - (df["TMetalFraction|transition metal fraction"] - df["ElementProperty|MagpieData mean NpValence"])
Feature 8: (abs(df["TMetalFraction|transition metal fraction"] - df["Stoichiometry|0-norm"])) - (df["TMetalFraction|transition metal fraction"] - df["ElementProperty|MagpieData mean Electronegativity"])
Feature 9: (df["ValenceOrbital|frac p valence electrons"] * df["TMetalFraction|transition metal fraction"]) - (df["ValenceOrbital|frac p valence electrons"] - df["TMetalFraction|transition metal fraction"])
Feature 10: abs(df["ValenceOrbital|frac p valence electrons"] - df["ElementProperty|MagpieData range NdValence"]) - exp(df["ElementProperty|MagpieData range NdValence"])
Feature 11: (df["TMetalFraction|transition metal fraction"] + df["AtomicOrbitals|HOMO_energy"]) * (abs(df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_energy"]))
Feature 12: (abs(df["TMetalFraction|transition metal fraction"] - df["Stoichiometry|0-norm"])) - (df["TMetalFraction|transition metal fraction"] - df["ElementProperty|MagpieData mean NpValence"])
Feature 13: abs(df["ValenceOrbital|frac p valence electrons"] - df["ElementProperty|MagpieData maximum NdValence"]) - exp(df["ElementProperty|MagpieData range NdValence"])
Feature 14: abs(df["ValenceOrbital|frac p valence electrons"] - df["ElementProperty|MagpieData maximum NdValence"]) - exp(df["ElementProperty|MagpieData maximum NdValence"])
Feature 15: (df["ValenceOrbital|frac p valence electrons"] - df["TMetalFraction|transition metal fraction"]) + (abs(df["TMetalFraction|transition metal fraction"] - df["Stoichiometry|0-norm"]))
Feature 16: (df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_energy"]) * (abs(df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_element"]))
Feature 17: (abs(df["IonProperty|max ionic char"] - df["ElementProperty|MagpieData avg_dev Electronegativity"])) * (abs(df["ElementFraction|Yb"] - df["AtomicOrbitals|gap_AO"]))
Feature 18: (df["ElementProperty|MagpieData avg_dev Electronegativity"] * df["AtomicOrbitals|gap_AO"]) + exp(df["AtomicOrbitals|gap_AO"])
Feature 19: (abs(df["AtomicOrbitals|gap_AO"] - df["AtomicOrbitals|HOMO_energy"])) * (df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_energy"])
Feature 20: (df["ElementProperty|MagpieData avg_dev Electronegativity"] * df["AtomicOrbitals|gap_AO"]) + df["AtomicOrbitals|gap_AO"]
Feature 21: (df["ElementProperty|MagpieData avg_dev NpValence"] * df["AtomicOrbitals|gap_AO"]) + exp(df["AtomicOrbitals|gap_AO"])
Feature 22: exp(df["ElementProperty|MagpieData avg_dev NpValence"]) * (abs(df["AtomicOrbitals|gap_AO"]))
Feature 23: (df["ElementProperty|MagpieData avg_dev NpValence"] * df["AtomicOrbitals|gap_AO"]) + df["AtomicOrbitals|gap_AO"]
Feature 24: safe_div((abs(df["ElementFraction|B"] - df["AtomicOrbitals|gap_AO"])), exp(df["ElementProperty|MagpieData mean NUnfilled"]))
Feature 25: exp(df["IonProperty|avg ionic char"]) * (abs(df["ElementFraction|B"] - df["AtomicOrbitals|gap_AO"]))
Feature 26: exp(df["ElementProperty|MagpieData avg_dev Column"]) * (df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_energy"])
Feature 27: (df["ElementProperty|MagpieData maximum NpUnfilled"] + df["ElementProperty|MagpieData maximum NdValence"]) * (df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_energy"])
Feature 28: safe_div((abs(df["ElementFraction|B"] - df["AtomicOrbitals|gap_AO"])), exp(df["AtomicOrbitals|HOMO_energy"]))
Feature 29: exp(df["ElementProperty|MagpieData avg_dev Electronegativity"]) * (abs(df["ElementFraction|B"] - df["AtomicOrbitals|gap_AO"]))
Feature 30: exp(df["ElementProperty|MagpieData avg_dev NpValence"]) * (abs(df["ElementFraction|B"] - df["AtomicOrbitals|gap_AO"]))
Feature 31: abs((df["TMetalFraction|transition metal fraction"] + df["ElementProperty|MagpieData avg_dev Electronegativity"]) - (df["ElementProperty|MagpieData mean AtomicWeight"] + df["AtomicOrbitals|LUMO_energy"]))
Feature 32: safe_div((abs(df["IonProperty|avg ionic char"] - df["AtomicOrbitals|LUMO_energy"])), (abs(df["ElementProperty|MagpieData minimum GSvolume_pa"] - df["AtomicOrbitals|HOMO_energy"])))
Feature 33: (df["ElementProperty|MagpieData maximum GSvolume_pa"] * df["ElementProperty|MagpieData range MendeleevNumber"]) + (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"])
Feature 34: (df["ElementProperty|MagpieData maximum GSvolume_pa"] * df["ElementProperty|MagpieData range Column"]) + (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"])
Feature 35: (abs(df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] - df["AtomicOrbitals|LUMO_energy"])) - (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"])
Feature 36: (abs(df["ElementProperty|MagpieData avg_dev SpaceGroupNumber"] - df["ElementFraction|B"])) - (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"])
Feature 37: (df["ElementProperty|MagpieData mean NpUnfilled"] * df["AtomicOrbitals|LUMO_energy"]) - (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"])
Feature 38: (abs(df["ElementProperty|MagpieData mode GSbandgap"] - df["ElementProperty|MagpieData minimum GSvolume_pa"])) - (df["ElementProperty|MagpieData mode GSbandgap"] + df["ElementProperty|MagpieData range NdValence"])
Feature 39: safe_div((abs(df["ElementProperty|MagpieData minimum GSvolume_pa"] - df["AtomicOrbitals|LUMO_energy"])), (abs(df["ElementProperty|MagpieData avg_dev Electronegativity"] - df["AtomicOrbitals|LUMO_energy"])))
Feature 40: (abs(df["IonProperty|max ionic char"] - df["AtomicOrbitals|LUMO_energy"])) - (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"])
Feature 41: abs((df["YangSolidSolution|Yang delta"] * df["ElementProperty|MagpieData maximum MendeleevNumber"]) - (df["ElementProperty|MagpieData avg_dev Electronegativity"] - df["AtomicOrbitals|LUMO_energy"]))
Feature 42: (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"]) - (abs(df["ElementProperty|MagpieData range Electronegativity"] - df["AtomicOrbitals|LUMO_energy"]))
Feature 43: (abs(df["IonProperty|avg ionic char"] - df["AtomicOrbitals|LUMO_energy"])) - (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"])
Feature 44: safe_div((abs(df["ElementProperty|MagpieData minimum GSvolume_pa"] - df["AtomicOrbitals|HOMO_energy"])), (abs(df["ElementProperty|MagpieData avg_dev Electronegativity"] - df["AtomicOrbitals|LUMO_energy"])))
Feature 45: (df["ElementProperty|MagpieData range NdValence"] - df["AtomicOrbitals|HOMO_element"]) - (abs(df["ElementProperty|MagpieData avg_dev Electronegativity"] - df["AtomicOrbitals|LUMO_energy"]))
Feature 46: safe_div((df["ElementFraction|Yb"] * df["AtomicOrbitals|LUMO_character"]), (df["ValenceOrbital|frac p valence electrons"] - df["ElementProperty|MagpieData minimum Number"]))
Feature 47: safe_div((df["ElementProperty|MagpieData maximum GSbandgap"] * df["ElementFraction|Yb"]), (df["IonProperty|max ionic char"] + df["ElementProperty|MagpieData range MendeleevNumber"]))
Feature 48: safe_div((df["YangSolidSolution|Yang delta"] * df["ElementFraction|Yb"]), (df["ValenceOrbital|frac s valence electrons"] + df["ElementProperty|MagpieData mean Electronegativity"]))
Feature 49: safe_div(safe_div(df["ElementFraction|Yb"], df["ElementProperty|MagpieData mean Electronegativity"]), (df["ElementProperty|MagpieData range Electronegativity"] - df["ElementProperty|MagpieData mean AtomicWeight"]))
Feature 50: safe_div((df["ElementFraction|Yb"] * df["AtomicOrbitals|HOMO_element"]), (df["ValenceOrbital|frac p valence electrons"] - df["ElementProperty|MagpieData minimum Number"]))
Feature 51: (df["ElementProperty|MagpieData maximum GSbandgap"] - df["ElementProperty|MagpieData avg_dev CovalentRadius"]) * (abs(df["ElementFraction|Yb"] - df["AtomicOrbitals|gap_AO"]))
Feature 52: abs((df["YangSolidSolution|Yang delta"] * df["ElementProperty|MagpieData range NdValence"]) - safe_div(df["ElementFraction|Yb"], df["ElementProperty|MagpieData mean CovalentRadius"]))
Feature 53: (df["ElementProperty|MagpieData maximum GSbandgap"] - df["ElementProperty|MagpieData avg_dev CovalentRadius"]) * (df["ElementFraction|Yb"] + df["AtomicOrbitals|gap_AO"])
Feature 54: safe_div((df["ElementFraction|Yb"] / df["ElementProperty|MagpieData mean Electronegativity"]), (df["ElementProperty|MagpieData mean CovalentRadius"] + df["ElementProperty|MagpieData mean AtomicWeight"]))
Feature 55: safe_div((df["ElementProperty|MagpieData range NdValence"] * df["ElementProperty|MagpieData maximum NdValence"]), (abs(df["ValenceOrbital|frac s valence electrons"] - df["ElementProperty|MagpieData maximum NpUnfilled"])))
Feature 56: (df["ValenceOrbital|frac p valence electrons"] * df["ElementFraction|B"]) + (df["ElementProperty|MagpieData avg_dev CovalentRadius"] * df["ElementFraction|Yb"])
Feature 57: safe_div((df["ElementProperty|MagpieData maximum NdValence"]**2), (abs(df["ValenceOrbital|frac s valence electrons"] - df["ElementProperty|MagpieData maximum NpUnfilled"])))
Feature 58: safe_div((df["ElementProperty|MagpieData range NdValence"]**2), (abs(df["ValenceOrbital|frac s valence electrons"] - df["ElementProperty|MagpieData maximum NpUnfilled"])))
Feature 59: (abs(df["Stoichiometry|0-norm"] - df["IonProperty|max ionic char"])) * (abs(df["ElementProperty|MagpieData maximum NdValence"] - df["AtomicOrbitals|HOMO_element"]))
Feature 60: safe_div((abs(df["ElementFraction|Yb"] - df["ElementFraction|B"])), (df["ElementProperty|MagpieData mean CovalentRadius"] + df["AtomicOrbitals|HOMO_energy"]))
Feature 61: (abs(df["Stoichiometry|0-norm"] - df["ElementProperty|MagpieData range Electronegativity"])) * (df["ElementProperty|MagpieData maximum NpUnfilled"] * df["ElementProperty|MagpieData maximum NdValence"])
Feature 62: safe_div((df["IonProperty|max ionic char"] / df["ValenceOrbital|frac s valence electrons"]), (abs(df["ElementProperty|MagpieData mean NdUnfilled"] - df["ElementProperty|MagpieData mean CovalentRadius"])))
Feature 63: (df["Stoichiometry|0-norm"] - df["ElementProperty|MagpieData range MendeleevNumber"]) * (df["ElementProperty|MagpieData maximum NdValence"] + df["ElementFraction|N"])
Feature 64: (df["Stoichiometry|0-norm"] - df["ElementProperty|MagpieData range MendeleevNumber"]) * (df["ElementProperty|MagpieData maximum NdValence"] + df["ElementFraction|Ga"])
Feature 65: safe_div((df["ElementProperty|MagpieData mean NpUnfilled"] * df["ElementProperty|MagpieData maximum NdValence"]), (abs(df["ValenceOrbital|frac s valence electrons"] - df["ElementProperty|MagpieData mode MeltingT"])))
Feature 66: safe_div((df["ElementProperty|MagpieData maximum NpUnfilled"] * df["ElementProperty|MagpieData range NdValence"]), (abs(df["ValenceOrbital|frac s valence electrons"] - df["ElementProperty|MagpieData mean AtomicWeight"])))
Feature 67: (df["Stoichiometry|0-norm"] - df["ElementProperty|MagpieData range MendeleevNumber"]) * (df["ElementProperty|MagpieData maximum NdValence"] - df["ElementProperty|MagpieData mean NpValence"])
Feature 68: (abs(df["Stoichiometry|0-norm"] - df["IonProperty|avg ionic char"])) * (df["ElementProperty|MagpieData maximum NpUnfilled"] * df["ElementProperty|MagpieData maximum NdValence"])
Feature 69: (abs(df["Stoichiometry|0-norm"] - df["ElementProperty|MagpieData range MendeleevNumber"])) * (df["ElementProperty|MagpieData maximum NpUnfilled"] * df["ElementProperty|MagpieData maximum NdValence"])
Feature 70: (df["ElementProperty|MagpieData maximum NpUnfilled"] + df["AtomicOrbitals|HOMO_energy"]) * (df["ElementProperty|MagpieData avg_dev CovalentRadius"] + df["AtomicOrbitals|LUMO_energy"])
Feature 71: safe_div((df["IonProperty|avg ionic char"] / df["ValenceOrbital|frac s valence electrons"]), (abs(df["ElementProperty|MagpieData mean NdUnfilled"] - df["ElementProperty|MagpieData mean CovalentRadius"])))
Feature 72: (abs(df["Stoichiometry|0-norm"] - df["ElementProperty|MagpieData avg_dev CovalentRadius"])) * (df["ElementProperty|MagpieData maximum NpUnfilled"] * df["ElementProperty|MagpieData maximum NdValence"])
Feature 73: safe_div((df["ElementProperty|MagpieData maximum NpUnfilled"] * df["ElementProperty|MagpieData maximum NdValence"]), (abs(df["ValenceOrbital|frac s valence electrons"] - df["ElementProperty|MagpieData mean AtomicWeight"])))
Feature 74: safe_div((df["Stoichiometry|0-norm"] * df["ElementProperty|MagpieData mean Electronegativity"]), (df["ElementProperty|MagpieData mean NpUnfilled"] + df["AtomicOrbitals|HOMO_energy"]))
Feature 75: abs((df["ElementProperty|MagpieData maximum NpUnfilled"] + df["ElementProperty|MagpieData maximum NdValence"]) - (df["AtomicOrbitals|LUMO_energy"] - df["AtomicOrbitals|HOMO_energy"]))
