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input_image.py
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# CONTRIBUTORS: * Ericsson Chenebuah, Michel Nganbe and Alain Tchagang
# Department of Mechanical Engineering, University of Ottawa, 75 Laurier Ave. East, Ottawa, ON, K1N 6N5 Canada
# Digital Technologies Research Centre, National Research Council of Canada, 1200 Montréal Road, Ottawa, ON, K1A 0R6 Canada
# * email: echen013@uottawa.ca
# (June-2023)
## THIS SOURCE CODE DEVELOPS THE INPUT IMAGE FROM SCRATCH FOR ALL INVESTIGATED PEROVSKITE STOICHIOMETRIES.
# Please note that code must be executed alongside all relevant spreadsheet data in the same file directory.
import pandas as pd
import numpy as np
abx3 = pd.read_csv('atoms_abx3.csv')
a2bbx6 = pd.read_csv('atoms_a2bbx6.csv')
aabbx6 = pd.read_csv('atoms_aabbx6.csv')
atomic_no = {
"H":1, "He":2, "Li":3, "Be":4, "B":5, "C":6, "N":7, "O":8, "F":9, "Ne":10, "Na":11, "Mg":12, "Al":13, "Si":14, "P":15, "S":16, "Cl":17, "Ar":18, "K":19, "Ca":20,
"Sc":21, "Ti":22, "V":23, "Cr":24, "Mn":25, "Fe":26, "Co":27, "Ni":28, "Cu":29, "Zn":30, "Ga":31, "Ge":32, "As":33, "Se":34, "Br":35, "Kr":36, "Rb":37, "Sr":38,
"Y":39, "Zr":40, "Nb":41, "Mo":42, "Tc":43, "Ru":44, "Rh":45, "Pd":46, "Ag":47, "Cd":48, "In":49, "Sn":50, "Sb":51, "Te":52, "I":53, "Xe":54, "Cs":55, "Ba":56,
"La":57, "Ce":58, "Pr":59, "Nd":60, "Pm":61, "Sm":62, "Eu":63, "Gd":64, "Tb":65, "Dy":66, "Ho":67, "Er":68, "Tm":69, "Yb":70, "Lu":71, "Hf":72, "Ta":73, "W":74,
"Re":75, "Os":76, "Ir":77, "Pt":78, "Au":79, "Hg":80, "Tl":81, "Pb":82, "Bi":83, "Po":84, "At":85, "Rn":86, "Fr":87, "Ra":88, "Ac":89, "Th":90, "Pa":91, "U":92,
"Np":93, "Pu":94, "Am":95, "Cm":96, "Bk":97, "Cf":98, "Es":99, "Fm":100, "Md":101, "No":102, "Lr":103
}
group_no = {
"H":1, "He":18, "Li":1, "Be":2, "B":13, "C":14, "N":15, "O":16, "F":17, "Ne":18, "Na":1, "Mg":2, "Al":13, "Si":14, "P":15, "S":16, "Cl":17, "Ar":18, "K":1, "Ca":2,
"Sc":3, "Ti":4, "V":5, "Cr":6, "Mn":7, "Fe":8, "Co":9, "Ni":10, "Cu":11, "Zn":12, "Ga":13, "Ge":14, "As":15, "Se":16, "Br":17, "Kr":18, "Rb":1, "Sr":2,
"Y":3, "Zr":4, "Nb":5, "Mo":6, "Tc":7, "Ru":8, "Rh":9, "Pd":10, "Ag":11, "Cd":12, "In":13, "Sn":14, "Sb":15, "Te":16, "I":17, "Xe":18, "Cs":1, "Ba":2,
"La":3, "Ce":4, "Pr":5, "Nd":6, "Pm":7, "Sm":8, "Eu":9, "Gd":10, "Tb":11, "Dy":12, "Ho":13, "Er":14, "Tm":15, "Yb":16, "Lu":17, "Hf":4, "Ta":5, "W":6,
"Re":7, "Os":8, "Ir":9, "Pt":10, "Au":11, "Hg":12, "Tl":13, "Pb":14, "Bi":15, "Po":16, "At":17, "Rn":18, "Fr":1, "Ra":2, "Ac":3, "Th":4, "Pa":5, "U":6,
"Np":7, "Pu":8, "Am":9, "Cm":10, "Bk":11, "Cf":12, "Es":13, "Fm":14, "Md":15, "No":16, "Lr":17
}
row_no = {
"H":1, "He":1, "Li":2, "Be":2, "B":2, "C":2, "N":2, "O":2, "F":2, "Ne":2, "Na":3, "Mg":3, "Al":3, "Si":3, "P":3, "S":3, "Cl":3, "Ar":3, "K":4, "Ca":4,
"Sc":4, "Ti":4, "V":4, "Cr":4, "Mn":4, "Fe":4, "Co":4, "Ni":4, "Cu":4, "Zn":4, "Ga":4, "Ge":4, "As":4, "Se":4, "Br":4, "Kr":4, "Rb":5, "Sr":5,
"Y":5, "Zr":5, "Nb":5, "Mo":5, "Tc":5, "Ru":5, "Rh":5, "Pd":5, "Ag":5, "Cd":5, "In":5, "Sn":5, "Sb":5, "Te":5, "I":5, "Xe":5, "Cs":6, "Ba":6,
"La":8, "Ce":8, "Pr":8, "Nd":8, "Pm":8, "Sm":8, "Eu":8, "Gd":8, "Tb":8, "Dy":8, "Ho":8, "Er":8, "Tm":8, "Yb":8, "Lu":8, "Hf":6, "Ta":6, "W":6,
"Re":6, "Os":6, "Ir":6, "Pt":6, "Au":6, "Hg":6, "Tl":6, "Pb":6, "Bi":6, "Po":6, "At":6, "Rn":6, "Fr":7, "Ra":7, "Ac":9, "Th":9, "Pa":9, "U":9,
"Np":9, "Pu":9, "Am":9, "Cm":9, "Bk":9, "Cf":9, "Es":9, "Fm":9, "Md":9, "No":9, "Lr":9
}
valence = {
"H":1, "He":2, "Li":1, "Be":2, "B":3, "C":4, "N":5, "O":6, "F":7, "Ne":8, "Na":1, "Mg":2, "Al":3, "Si":4, "P":5, "S":6, "Cl":7, "Ar":8, "K":1, "Ca":2,
"Sc":2, "Ti":2, "V":2, "Cr":1, "Mn":2, "Fe":2, "Co":2, "Ni":2, "Cu":1, "Zn":2, "Ga":3, "Ge":4, "As":5, "Se":6, "Br":7, "Kr":8, "Rb":1, "Sr":2,
"Y":2, "Zr":2, "Nb":1, "Mo":1, "Tc":2, "Ru":1, "Rh":1, "Pd":9, "Ag":1, "Cd":2, "In":3, "Sn":4, "Sb":5, "Te":6, "I":7, "Xe":8, "Cs":1, "Ba":2,
"La":2, "Ce":2, "Pr":2, "Nd":2, "Pm":2, "Sm":2, "Eu":2, "Gd":2, "Tb":2, "Dy":2, "Ho":2, "Er":2, "Tm":2, "Yb":2, "Lu":2, "Hf":2, "Ta":2, "W":2,
"Re":2, "Os":2, "Ir":2, "Pt":1, "Au":1, "Hg":2, "Tl":3, "Pb":4, "Bi":5, "Po":6, "At":7, "Rn":8, "Fr":1, "Ra":2, "Ac":2, "Th":2, "Pa":2, "U":2,
"Np":2, "Pu":2, "Am":2, "Cm":2, "Bk":2, "Cf":2, "Es":2, "Fm":2, "Md":2, "No":2, "Lr":3
}
block = {
"H":1, "He":1, "Li":1, "Be":1, "B":2, "C":2, "N":2, "O":2, "F":2, "Ne":2, "Na":1, "Mg":1, "Al":2, "Si":2, "P":2, "S":2, "Cl":2, "Ar":2, "K":1, "Ca":1,
"Sc":3, "Ti":3, "V":3, "Cr":3, "Mn":3, "Fe":3, "Co":3, "Ni":3, "Cu":3, "Zn":3, "Ga":2, "Ge":2, "As":2, "Se":2, "Br":2, "Kr":2, "Rb":1, "Sr":1,
"Y":3, "Zr":3, "Nb":3, "Mo":3, "Tc":3, "Ru":3, "Rh":3, "Pd":3, "Ag":3, "Cd":3, "In":2, "Sn":2, "Sb":2, "Te":2, "I":2, "Xe":2, "Cs":1, "Ba":1,
"La":4, "Ce":4, "Pr":4, "Nd":4, "Pm":4, "Sm":4, "Eu":4, "Gd":4, "Tb":4, "Dy":4, "Ho":4, "Er":4, "Tm":4, "Yb":4, "Lu":3, "Hf":3, "Ta":3, "W":3,
"Re":3, "Os":3, "Ir":3, "Pt":3, "Au":3, "Hg":3, "Tl":2, "Pb":2, "Bi":2, "Po":2, "At":2, "Rn":2, "Fr":1, "Ra":1, "Ac":4, "Th":4, "Pa":4, "U":4,
"Np":4, "Pu":4, "Am":4, "Cm":4, "Bk":4, "Cf":4, "Es":4, "Fm":4, "Md":4, "No":4, "Lr":3
}
electronegativity = {
'H':2.2, 'He':'n.a', 'Li':0.98, 'Be': 1.57, 'B': 2.04, 'C':2.55,'N': 3.04,'O': 3.44,'F': 3.98,'Ne': "n.a",'Na': 0.93,'Mg': 1.31,'Al': 1.61,'Si': 1.9,
'P': 2.19,'S': 2.58,'Cl': 3.16,'Ar': "n.a", 'K': 0.82, 'Ca': 1, 'Sc': 1.36, 'Ti': 1.54, 'V': 1.63,'Cr': 1.66,'Mn': 1.55,'Fe': 1.83,'Co': 1.88,'Ni': 1.91,
'Cu': 1.9, 'Zn': 1.65, 'Ga': 1.81, 'Ge': 2.01,'As': 2.18,'Se': 2.55,'Br': 2.96,'Kr': "n.a",'Rb': 0.82,'Sr': 0.95,'Y': 1.22,'Zr': 1.33,'Nb': 1.6,'Mo': 2.16,
'Tc': 2.1,'Ru': 2.2,'Rh': 2.28,'Pd': 2.2,'Ag': 1.93,'Cd': 1.69,'In': 1.78,'Sn': 1.96,'Sb': 2.05,'Te': 2.1,'I': 2.66,'Xe': 2.6,'Cs': 0.79,'Ba': 0.89,
'La': 1.1,'Ce': 1.12,'Pr': 1.13,'Nd': 1.14,'Pm': 1.13,'Sm': 1.17,'Eu': 1.2,'Gd': 1.2,'Tb': 1.1,'Dy': 1.22,'Ho': 1.23,'Er': 1.24,'Tm': 1.25,'Yb': 1.1,
'Lu': 1,'Hf': 1.3,'Ta': 1.5,'W': 1.7,'Re': 1.9,'Os': 2.2,'Ir': 2.2,'Pt': 2.2,'Au': 2.4,'Hg': 1.9,'Tl': 1.8,'Pb': 1.8,'Bi': 1.9,'Po': 2,'At': 2.2,'Rn': 2.2,'Fr': 0.7,
'Ra': 0.9,'Ac': 1.1,'Th': 1.3,'Pa': 1.5,'U': 1.7,'Np': 1.3,'Pu': 1.3,'Am': 1.3,'Cm': 1.3,'Bk': 1.3,'Cf': 1.3,'Es': 1.3,'Fm': 1.3,'Md': 1.3,'No': 1.3,'Lr': 1.3
}
covalent_radius = {
'H': 0.31,'He': 0.28,'Li': 1.28,'Be': 0.96,'B': 0.84,'C': 0.76,'N': 0.71,'O': 0.66,'F': 0.57,'Ne': 0.58,'Na': 1.66,'Mg': 1.41,'Al': 1.21,'Si': 1.11,'P': 1.07,
'S': 1.05,'Cl': 1.02,'Ar': 1.06,'K': 2.03,'Ca': 1.76,'Sc': 1.7,'Ti': 1.6,'V': 1.53,'Cr': 1.39,'Mn': 1.39,'Fe': 1.32,'Co': 1.26,'Ni': 1.24,'Cu': 1.32,'Zn': 1.22,
'Ga': 1.22,'Ge': 1.2,'As': 1.19,'Se': 1.2,'Br': 1.2,'Kr': 1.16,'Rb': 2.2,'Sr': 1.95,'Y': 1.9,'Zr': 1.75,'Nb': 1.64,'Mo': 1.54,'Tc': 1.47,'Ru': 1.46,'Rh': 1.42,
'Pd': 1.39,'Ag': 1.45,'Cd': 1.44,'In': 1.42,'Sn': 1.39,'Sb': 1.39,'Te': 1.38,'I': 1.39,'Xe': 1.4,'Cs': 2.44,'Ba': 2.15,'La': 2.07,'Ce': 2.04,'Pr': 2.03,'Nd': 2.01,
'Pm': 1.99,'Sm': 1.98,'Eu': 1.98,'Gd': 1.96,'Tb': 1.94,'Dy': 1.92,'Ho': 1.92,'Er': 1.89,'Tm': 1.9,'Yb': 1.87,'Lu': 1.87,'Hf': 1.75,'Ta': 1.7,'W': 1.62,'Re': 1.51,
'Os': 1.44,'Ir': 1.41,'Pt': 1.36,'Au': 1.36,'Hg': 1.32,'Tl': 1.45,'Pb': 1.46,'Bi': 1.48,'Po': 1.4,'At': 1.5,'Rn': 1.5,'Fr': 2.6,'Ra': 2.21,'Ac': 2.15,'Th': 2.06,
'Pa': 2,'U': 1.96,'Np': 1.9,'Pu': 1.87,'Am': 1.8,'Cm': 1.69,'Bk': 'n.a','Cf': 'n.a','Es': 'n.a','Fm': 'n.a','Md': 'n.a','No': 'n.a','Lr': 'n.a'
}
ionization_energy = {
'H': 13.59844,'He': 24.58741,'Li': 5.39172,'Be': 9.3227,'B': 8.29803,'C': 11.2603,'N': 14.53414,'O': 13.61806,'F': 17.42282,'Ne': 21.5646,'Na': 5.13908,'Mg': 7.64624,
'Al': 5.98577,'Si': 8.15169,'P': 10.48669,'S': 10.36001,'Cl': 12.96764,'Ar': 15.75962,'K': 4.34066,'Ca': 6.11316,'Sc': 6.5615,'Ti': 6.8281,'V': 6.7462,'Cr': 6.7665,
'Mn': 7.43402,'Fe': 7.9024,'Co': 7.881,'Ni': 7.6398,'Cu': 7.72638,'Zn': 9.3942,'Ga': 5.9993,'Ge': 7.8994,'As': 9.7886,'Se': 9.75238,'Br': 11.81381,'Kr': 13.99961,
'Rb': 4.17713,'Sr': 5.6949,'Y': 6.2171,'Zr': 6.6339,'Nb': 6.75885,'Mo': 7.09243,'Tc': 7.28,'Ru': 7.3605,'Rh': 7.4589,'Pd': 8.3369,'Ag': 7.5762,'Cd': 8.9938,'In': 5.78636,
'Sn': 7.3439,'Sb': 8.6084,'Te': 9.0096,'I': 10.45126,'Xe': 12.1298,'Cs': 3.8939,'Ba': 5.2117,'La': 5.5769,'Ce': 5.5387,'Pr': 5.473,'Nd': 5.525,'Pm': 5.582,'Sm': 5.6436,
'Eu': 5.6704,'Gd': 6.1501,'Tb': 5.8638,'Dy': 5.9389,'Ho': 6.0215,'Er': 6.1077,'Tm': 6.18431,'Yb': 6.25416,'Lu': 5.4259,'Hf': 6.82507,'Ta': 7.5496,'W': 7.864,'Re': 7.8335,
'Os': 8.4382,'Ir': 8.967,'Pt': 8.9587,'Au': 9.2255,'Hg': 10.4375,'Tl': 6.1082,'Pb': 7.41666,'Bi': 7.2856,'Po': 8.417,'At': "n.a",'Rn': 10.7485,'Fr': 4.0727,'Ra': 5.2784,
'Ac': 5.17,'Th': 6.3067,'Pa': 5.89,'U': 6.19405,'Np': 6.2657,'Pu': 6.0262,'Am': 5.9738,'Cm': 5.9915,'Bk': 6.1979,'Cf': 6.2817,'Es': 6.42,'Fm': 6.5,'Md': 6.58,'No': 6.65,
'Lr': 4.9
}
electron_affinity = {
'H': 0.754195,'He': -0.5182,'Li': 0.618049,'Be': -0.5182,'B': 0.279723,'C': 1.262119,'N': -0.0725,'O': 1.4611096,'F': 3.4011895,'Ne': -1.2437,'Na': 0.547926,'Mg': -0.4146,
'Al': 0.43283,'Si': 1.389522,'P': 0.7465,'S': 2.077103,'Cl': 3.612724,'Ar': -0.995,'K': 0.50147,'Ca': 0.02455,'Sc': 0.188,'Ti': 0.079,'V': 0.525,'Cr': 0.666,'Mn': -0.5,
'Fe': 0.151,'Co': 0.662,'Ni': 1.156,'Cu': 1.235,'Zn': -0.6219,'Ga': 0.43,'Ge': 1.232712,'As': 0.814,'Se': 2.02067,'Br': 3.363588,'Kr': -1,'Rb': 0.48592,'Sr': 0.048,'Y': 0.307,
'Zr': 0.426,'Nb': 0.8933,'Mo': 0.748,'Tc': 0.55,'Ru': 1.05,'Rh': 1.137,'Pd': 0.562,'Ag': 1.302,'Cd': -0.7255,'In': 0.3,'Sn': 1.112067,'Sb': 1.046,'Te': 1.9708,'I': 3.059037,
'Xe': -0.8291,'Cs': 0.471626,'Ba': 0.14462,'La': 0.47,'Ce': 0.57,'Pr': 0.964,'Nd': 0.09749,'Pm': 0.124,'Sm': 0.166,'Eu': 0.114,'Gd': 0.135,'Tb': 1.161,'Dy': 0.352,'Ho': 0.342,
'Er': 0.311,'Tm': 1.03,'Yb': -0.02,'Lu': 0.34,'Hf': 0.176,'Ta': 0.322,'W': 0.815,'Re': 0.15,'Os': 1.1,'Ir': 1.5638,'Pt': 2.128,'Au': 2.30863,'Hg': -0.5182,'Tl': 0.2,'Pb': 0.364,
'Bi': 0.946,'Po': 1.9,'At': 2.8,'Rn': -0.7255,'Fr': 0.46,'Ra': 0.1,'Ac': 0.35,'Th': 0.60769,'Pa': 0.57,'U': 0.31497,'Np': 0.477,'Pu': -0.5,'Am': 0.1,'Cm': 0.28,'Bk': -1.72,
'Cf': -1.01,'Es': -0.3,'Fm': 0.35,'Md': 0.98,'No': -2.33,'Lr': -0.31
}
molar_volume = {
'H': 11.42,'He': 21,'Li': 13.02,'Be': 4.85,'B': 4.39,'C': 5.29,'N': 13.54,'O': 17.36,'F': 11.2,'Ne': 13.23,'Na': 23.78,'Mg': 14,'Al': 10,'Si': 12.06,'P': 17.02,'S': 15.53,
'Cl': 17.39,'Ar': 22.56,'K': 45.94,'Ca': 26.2,'Sc': 15,'Ti': 10.64,'V': 8.32,'Cr': 7.23,'Mn': 7.35,'Fe': 7.09,'Co': 6.67,'Ni': 6.59,'Cu': 7.11,'Zn': 9.16,'Ga': 11.8,
'Ge': 13.63,'As': 12.95,'Se': 16.42,'Br': 19.78,'Kr': 27.99,'Rb': 55.76,'Sr': 33.94,'Y': 19.88,'Zr': 14.02,'Nb': 10.83,'Mo': 9.38,'Tc': 8.63,'Ru': 8.17,'Rh': 8.28,'Pd': 8.56,
'Ag': 10.27,'Cd': 13,'In': 15.76,'Sn': 16.29,'Sb': 18.19,'Te': 20.46,'I': 25.72,'Xe': 35.92,'Cs': 70.94,'Ba': 38.16,'La': 22.39,'Ce': 20.69,'Pr': 20.8,'Nd': 20.59,'Pm': 20.23,
'Sm': 19.98,'Eu': 28.97,'Gd': 19.9,'Tb': 19.3,'Dy': 19.01,'Ho': 18.74,'Er': 18.46,'Tm': 19.1,'Yb': 24.84,'Lu': 17.78,'Hf': 13.44,'Ta': 10.85,'W': 9.47,'Re': 8.86,'Os': 8.42,
'Ir': 8.52,'Pt': 9.09,'Au': 10.21,'Hg': 14.09,'Tl': 17.22,'Pb': 18.26,'Bi': 21.31,'Po': 22.97,'At': "n.a",'Rn': 50.5,'Fr': "n.a",'Ra': 41.09,'Ac': 22.55,'Th': 19.8,'Pa': 15.18,
'U': 12.49,'Np': 11.59,'Pu': 12.29,'Am': 17.63,'Cm': 18.05,'Bk': 16.84,'Cf': 16.5,'Es': 28.52,'Fm': "n.a",'Md': "n.a",'No': "n.a",'Lr': "n.a"
}
average_ionic_radius = {
'H': 0,'He': 0,'Li': 0.9,'Be': 0.59,'B': 0.41,'C': 0.3,'N': 0.63,'O': 1.26,'F': 0.705,'Ne': 0,'Na': 1.16,'Mg': 0.86,'Al': 0.675,'Si': 0.54,'P': 0.55,'S': 0.88,'Cl': 0.78,
'Ar': 0,'K': 1.52,'Ca': 1.14,'Sc': 0.885,'Ti': 0.852,'V': 0.777,'Cr': 0.94,'Mn': 0.648,'Fe': 0.853,'Co': 0.768,'Ni': 0.74,'Cu': 0.82,'Zn': 0.88,'Ga': 0.76,'Ge': 0.77,
'As': 0.66,'Se': 1.013,'Br': 0.883,'Kr': 0,'Rb': 1.66,'Sr': 1.32,'Y': 1.04,'Zr': 0.86,'Nb': 0.82,'Mo': 0.775,'Tc': 0.742,'Ru': 0.661,'Rh': 0.745,'Pd': 0.846,'Ag': 1.087,
'Cd': 1.09,'In': 0.94,'Sn': 0.83,'Sb': 0.83,'Te': 1.293,'I': 1.273,'Xe': 0.62,'Cs': 1.81,'Ba': 1.49,'La': 1.172,'Ce': 1.08,'Pr': 1.06,'Nd': 1.276,'Pm': 1.11,'Sm': 1.229,
'Eu': 1.199,'Gd': 1.075,'Tb': 0.982,'Dy': 1.131,'Ho': 1.041,'Er': 1.03,'Tm': 1.095,'Yb': 1.084,'Lu': 1.001,'Hf': 0.85,'Ta': 0.82,'W': 0.767,'Re': 0.712,'Os': 0.673,'Ir': 0.765,
'Pt': 0.805,'Au': 1.07,'Hg': 1.245,'Tl': 1.333,'Pb': 1.123,'Bi': 1.035,'Po': 0.945,'At': 0.76,'Rn': 0,'Fr': 1.94,'Ra': 1.62,'Ac': 1.26,'Th': 1.08,'Pa': 1.04,'U': 0.991,'Np': 1,
'Pu': 0.967,'Am': 1.168,'Cm': 1.05,'Bk': 1.035,'Cf': 1.026,'Es': 0,'Fm': 0,'Md': 0,'No': 0,'Lr': 0
}
polarizability = {
'H': 0.666793,'He': 0.204956,'Li': 24.3,'Be': 5.6,'B': 3.03,'C': 1.76,'N': 1.1,'O': 0.802,'F': 0.557,'Ne': 0.3956,'Na': 24.11,'Mg': 10.6,'Al': 6.8,'Si': 5.38,'P': 3.63,
'S': 2.9,'Cl': 2.18,'Ar': 1.6411,'K': 43.4,'Ca': 22.8,'Sc': 17.8,'Ti': 14.6,'V': 12.4,'Cr': 11.6,'Mn': 9.4,'Fe': 8.4,'Co': 7.5,'Ni': 6.8,'Cu': 6.2,'Zn': 5.75,'Ga': 8.12,
'Ge': 6.07,'As': 4.31,'Se': 3.77,'Br': 3.05,'Kr': 2.4844,'Rb': 47.3,'Sr': 27.6,'Y': 22.7,'Zr': 17.9,'Nb': 15.7,'Mo': 12.8,'Tc': 11.4,'Ru': 9.6,'Rh': 8.6,'Pd': 4.8,
'Ag': 7.2,'Cd': 7.36,'In': 10.2,'Sn': 7.7,'Sb': 6.6,'Te': 5.5,'I': 5.35,'Xe': 4.044,'Cs': 59.42,'Ba': 39.7,'La': 31.1,'Ce': 29.6,'Pr': 28.2,'Nd': 31.4,'Pm': 30.1,
'Sm': 28.8,'Eu': 27.7,'Gd': 23.5,'Tb': 25.5,'Dy': 24.5,'Ho': 23.6,'Er': 22.7,'Tm': 21.8,'Yb': 21,'Lu': 21.9,'Hf': 16.2,'Ta': 13.1,'W': 11.1,'Re': 9.7,'Os': 8.5,
'Ir': 7.6,'Pt': 6.5,'Au': 5.8,'Hg': 5.02,'Tl': 7.6,'Pb': 6.8,'Bi': 7.4,'Po': 6.8,'At': 6,'Rn': 5.3,'Fr': 47.1,'Ra': 38.3,'Ac': 32.1,'Th': 32.1,'Pa': 25.4,'U': 24.9,'Np': 24.8,
'Pu': 24.5,'Am': 23.3,'Cm': 23,'Bk': 22.7,'Cf': 20.5,'Es': 19.7,'Fm': 23.8,'Md': 18.2,'No': 17.5,'Lr': "n.a."
}
specific_heat = {
'H': 14.304,'He': 5.193,'Li': 3.582,'Be': 1.825,'B': 1.026,'C': 0.709,'N': 1.04,'O': 0.918,'F': 0.824,'Ne': 1.03,'Na': 1.228,'Mg': 1.023,'Al': 0.897,'Si': 0.705,'P': 0.769,
'S': 0.71,'Cl': 0.479,'Ar': 0.52,'K': 0.757,'Ca': 0.647,'Sc': 0.568,'Ti': 0.523,'V': 0.489,'Cr': 0.449,'Mn': 0.479,'Fe': 0.449,'Co': 0.421,'Ni': 0.444,'Cu': 0.385,'Zn': 0.388,
'Ga': 0.371,'Ge': 0.32,'As': 0.329,'Se': 0.321,'Br': 0.474,'Kr': 0.248,'Rb': 0.363,'Sr': 0.301,'Y': 0.298,'Zr': 0.278,'Nb': 0.265,'Mo': 0.251,'Tc': 0.063,'Ru': 0.238,'Rh': 0.243,
'Pd': 0.244,'Ag': 0.235,'Cd': 0.232,'In': 0.233,'Sn': 0.228,'Sb': 0.207,'Te': 0.202,'I': 0.214,'Xe': 0.158,'Cs': 0.242,'Ba': 0.204,'La': 0.195,'Ce': 0.192,'Pr': 0.193,'Nd': 0.19,
'Pm': 0.18,'Sm': 0.197,'Eu': 0.182,'Gd': 0.236,'Tb': 0.182,'Dy': 0.17,'Ho': 0.165,'Er': 0.168,'Tm': 0.16,'Yb': 0.155,'Lu': 0.154,'Hf': 0.144,'Ta': 0.14,'W': 0.132,'Re': 0.137,
'Os': 0.13,'Ir': 0.131,'Pt': 0.133,'Au': 0.129,'Hg': 0.14,'Tl': 0.129,'Pb': 0.129,'Bi': 0.122,'Po': "n.a",'At': "n.a",'Rn': 0.094,'Fr': "n.a",'Ra': 0.092,'Ac': 0.12,'Th': 0.113,
'Pa': 0.0991,'U': 0.116,'Np': 0.12,'Pu': 0.13,'Am': "n.a",'Cm': "n.a",'Bk': "n.a",'Cf': "n.a",'Es': "n.a",'Fm': "n.a",'Md': "n.a",'No': "n.a",'Lr': "n.a"
}
thermal_conductivity = {
'H': 0.1805,'He': 0.1513,'Li': 85,'Be': 190,'B': 27,'C': 140,'N': 0.02583,'O': 0.02658,'F': 0.0277,'Ne': 0.0491,'Na': 140,'Mg': 160,'Al': 235,'Si': 150,'P': 0.236,
'S': 0.205,'Cl': 0.0089,'Ar': 0.01772,'K': 100,'Ca': 200,'Sc': 16,'Ti': 22,'V': 31,'Cr': 94,'Mn': 7.8,'Fe': 80,'Co': 100,'Ni': 91,'Cu': 400,'Zn': 120,'Ga': 29,'Ge': 60,
'As': 50,'Se': 2.04,'Br': 0.12,'Kr': 0.00943,'Rb': 58,'Sr': 35,'Y': 17,'Zr': 23,'Nb': 54,'Mo': 139,'Tc': 51,'Ru': 120,'Rh': 150,'Pd': 72,'Ag': 430,'Cd': 97,'In': 82,
'Sn': 67,'Sb': 24,'Te': 3,'I': 0.449,'Xe': 0.00565,'Cs': 36,'Ba': 18,'La': 13,'Ce': 11,'Pr': 13,'Nd': 17,'Pm': 17.9,'Sm': 13,'Eu': 14,'Gd': 11,'Tb': 11,'Dy': 11,
'Ho': 16,'Er': 15,'Tm': 17,'Yb': 39,'Lu': 16,'Hf': 23,'Ta': 57,'W': 170,'Re': 48,'Os': 88,'Ir': 150,'Pt': 72,'Au': 320,'Hg': 8.3,'Tl': 46,'Pb': 35,'Bi': 8,'Po': 20,
'At': 2,'Rn': 0.00361,'Fr': 77,'Ra': 19,'Ac': 12,'Th': 54,'Pa': 47,'U': 27,'Np': 6,'Pu': 6,'Am': 10,'Cm': 8.8,'Bk': 10,'Cf': 10,'Es': 10,'Fm': 10,'Md': 10,'No': 10,
'Lr': 10
}
# Atomic Number
abx3_datasize = abx3.shape[0]
a2bbx6_datasize = a2bbx6.shape[0]
aabbx6_datasize = aabbx6.shape[0]
data1=[]
for i in range (0, abx3_datasize):
z=atomic_no[abx3.iloc[(i,0)]], atomic_no[abx3.iloc[(i,1)]], atomic_no[abx3.iloc[(i,2)]]
data1.append(z)
Z_abx3=(np.array(data1))-1
data1=[]
for i in range (0, a2bbx6_datasize):
z=atomic_no[a2bbx6.iloc[(i,0)]], atomic_no[a2bbx6.iloc[(i,1)]], atomic_no[a2bbx6.iloc[(i,2)]], atomic_no[a2bbx6.iloc[(i,3)]]
data1.append(z)
Z_a2bbx6=(np.array(data1))-1
data1=[]
for i in range (0, aabbx6_datasize):
z=atomic_no[aabbx6.iloc[(i,0)]], atomic_no[aabbx6.iloc[(i,1)]], atomic_no[aabbx6.iloc[(i,2)]], atomic_no[aabbx6.iloc[(i,3)]], atomic_no[aabbx6.iloc[(i,4)]]
data1.append(z)
Z_aabbx6=(np.array(data1))-1
# Group Number
data2=[]
for i in range (0, abx3_datasize):
gn=group_no[abx3.iloc[(i,0)]], group_no[abx3.iloc[(i,1)]], group_no[abx3.iloc[(i,2)]]
data2.append(gn)
GN_abx3=(np.array(data2))-1
data2=[]
for i in range (0, a2bbx6_datasize):
gn=group_no[a2bbx6.iloc[(i,0)]], group_no[a2bbx6.iloc[(i,1)]], group_no[a2bbx6.iloc[(i,2)]], group_no[a2bbx6.iloc[(i,3)]]
data2.append(gn)
GN_a2bbx6=(np.array(data2))-1
data2=[]
for i in range (0, aabbx6_datasize):
gn=group_no[aabbx6.iloc[(i,0)]], group_no[aabbx6.iloc[(i,1)]], group_no[aabbx6.iloc[(i,2)]], group_no[aabbx6.iloc[(i,3)]], group_no[aabbx6.iloc[(i,4)]]
data2.append(gn)
GN_aabbx6=(np.array(data2))-1
# Row Number
data3=[]
for i in range (0, abx3_datasize):
rn=row_no[abx3.iloc[(i,0)]], row_no[abx3.iloc[(i,1)]], row_no[abx3.iloc[(i,2)]]
data3.append(rn)
RN_abx3=(np.array(data3))-1
data3=[]
for i in range (0, a2bbx6_datasize):
rn=row_no[a2bbx6.iloc[(i,0)]], row_no[a2bbx6.iloc[(i,1)]], row_no[a2bbx6.iloc[(i,2)]], row_no[a2bbx6.iloc[(i,3)]]
data3.append(rn)
RN_a2bbx6=(np.array(data3))-1
data3=[]
for i in range (0, aabbx6_datasize):
rn=row_no[aabbx6.iloc[(i,0)]], row_no[aabbx6.iloc[(i,1)]], row_no[aabbx6.iloc[(i,2)]], row_no[aabbx6.iloc[(i,3)]], row_no[aabbx6.iloc[(i,4)]]
data3.append(rn)
RN_aabbx6=(np.array(data3))-1
# Valence
data4=[]
for i in range (0, abx3_datasize):
vl=valence[abx3.iloc[(i,0)]], valence[abx3.iloc[(i,1)]], valence[abx3.iloc[(i,2)]]
data4.append(vl)
VL_abx3=(np.array(data4))-1
data4=[]
for i in range (0, a2bbx6_datasize):
vl=valence[a2bbx6.iloc[(i,0)]], valence[a2bbx6.iloc[(i,1)]], valence[a2bbx6.iloc[(i,2)]], valence[a2bbx6.iloc[(i,3)]]
data4.append(vl)
VL_a2bbx6=(np.array(data4))-1
data4=[]
for i in range (0, aabbx6_datasize):
vl=valence[aabbx6.iloc[(i,0)]], valence[aabbx6.iloc[(i,1)]], valence[aabbx6.iloc[(i,2)]], valence[aabbx6.iloc[(i,3)]], valence[aabbx6.iloc[(i,4)]]
data4.append(vl)
VL_aabbx6=(np.array(data4))-1
# Block
data5=[]
for i in range (0, abx3_datasize):
bk=block[abx3.iloc[(i,0)]], block[abx3.iloc[(i,1)]], block[abx3.iloc[(i,2)]]
data5.append(bk)
BK_abx3=(np.array(data5))-1
data5=[]
for i in range (0, a2bbx6_datasize):
bk=block[a2bbx6.iloc[(i,0)]], block[a2bbx6.iloc[(i,1)]], block[a2bbx6.iloc[(i,2)]], block[a2bbx6.iloc[(i,3)]]
data5.append(bk)
BK_a2bbx6=(np.array(data5))-1
data5=[]
for i in range (0, aabbx6_datasize):
bk=block[aabbx6.iloc[(i,0)]], block[aabbx6.iloc[(i,1)]], block[aabbx6.iloc[(i,2)]], block[aabbx6.iloc[(i,3)]], block[aabbx6.iloc[(i,4)]]
data5.append(bk)
BK_aabbx6=(np.array(data5))-1
# Electronegativity
data6=[]
for i in range (0, abx3_datasize):
x=electronegativity[abx3.iloc[(i,0)]], electronegativity[abx3.iloc[(i,1)]], electronegativity[abx3.iloc[(i,2)]]
data6.append(x)
X_abx3=(np.array(data6))
data6=[]
for i in range (0, a2bbx6_datasize):
x=electronegativity[a2bbx6.iloc[(i,0)]], electronegativity[a2bbx6.iloc[(i,1)]], electronegativity[a2bbx6.iloc[(i,2)]], electronegativity[a2bbx6.iloc[(i,3)]]
data6.append(x)
X_a2bbx6=(np.array(data6))
data6=[]
for i in range (0, aabbx6_datasize):
x=(electronegativity[aabbx6.iloc[(i,0)]], electronegativity[aabbx6.iloc[(i,1)]], electronegativity[aabbx6.iloc[(i,2)]],
electronegativity[aabbx6.iloc[(i,3)]], electronegativity[aabbx6.iloc[(i,4)]])
data6.append(x)
X_aabbx6=(np.array(data6))
# Covalent Radius
data7=[]
for i in range (0, abx3_datasize):
cr=covalent_radius[abx3.iloc[(i,0)]], covalent_radius[abx3.iloc[(i,1)]], covalent_radius[abx3.iloc[(i,2)]]
data7.append(cr)
CR_abx3=(np.array(data7))
data7=[]
for i in range (0, a2bbx6_datasize):
cr=covalent_radius[a2bbx6.iloc[(i,0)]], covalent_radius[a2bbx6.iloc[(i,1)]], covalent_radius[a2bbx6.iloc[(i,2)]], covalent_radius[a2bbx6.iloc[(i,3)]]
data7.append(cr)
CR_a2bbx6=(np.array(data7))
data7=[]
for i in range (0, aabbx6_datasize):
cr=covalent_radius[aabbx6.iloc[(i,0)]], covalent_radius[aabbx6.iloc[(i,1)]], covalent_radius[aabbx6.iloc[(i,2)]], covalent_radius[aabbx6.iloc[(i,3)]], covalent_radius[aabbx6.iloc[(i,4)]]
data7.append(cr)
CR_aabbx6=(np.array(data7))
# Ionization Energy
data8=[]
for i in range (0, abx3_datasize):
ie=ionization_energy[abx3.iloc[(i,0)]], ionization_energy[abx3.iloc[(i,1)]], ionization_energy[abx3.iloc[(i,2)]]
data8.append(ie)
IE_abx3=(np.array(data8))
data8=[]
for i in range (0, a2bbx6_datasize):
ie=ionization_energy[a2bbx6.iloc[(i,0)]], ionization_energy[a2bbx6.iloc[(i,1)]], ionization_energy[a2bbx6.iloc[(i,2)]], ionization_energy[a2bbx6.iloc[(i,3)]]
data8.append(ie)
IE_a2bbx6=(np.array(data8))
data8=[]
for i in range (0, aabbx6_datasize):
ie=(ionization_energy[aabbx6.iloc[(i,0)]], ionization_energy[aabbx6.iloc[(i,1)]], ionization_energy[aabbx6.iloc[(i,2)]],
ionization_energy[aabbx6.iloc[(i,3)]], ionization_energy[aabbx6.iloc[(i,4)]])
data8.append(ie)
IE_aabbx6=(np.array(data8))
# Electron Affinity
data9=[]
for i in range (0, abx3_datasize):
ea=electron_affinity[abx3.iloc[(i,0)]], electron_affinity[abx3.iloc[(i,1)]], electron_affinity[abx3.iloc[(i,2)]]
data9.append(ea)
EA_abx3=(np.array(data9))
data9=[]
for i in range (0, a2bbx6_datasize):
ea=electron_affinity[a2bbx6.iloc[(i,0)]], electron_affinity[a2bbx6.iloc[(i,1)]], electron_affinity[a2bbx6.iloc[(i,2)]], electron_affinity[a2bbx6.iloc[(i,3)]]
data9.append(ea)
EA_a2bbx6=(np.array(data9))
data9=[]
for i in range (0, aabbx6_datasize):
ea=(electron_affinity[aabbx6.iloc[(i,0)]], electron_affinity[aabbx6.iloc[(i,1)]], electron_affinity[aabbx6.iloc[(i,2)]],
electron_affinity[aabbx6.iloc[(i,3)]], electron_affinity[aabbx6.iloc[(i,4)]])
data9.append(ea)
EA_aabbx6=(np.array(data9))
# Molar Volume
data10=[]
for i in range (0, abx3_datasize):
mv=molar_volume[abx3.iloc[(i,0)]], molar_volume[abx3.iloc[(i,1)]], molar_volume[abx3.iloc[(i,2)]]
data10.append(mv)
MV_abx3=(np.array(data10))
data10=[]
for i in range (0, a2bbx6_datasize):
mv=molar_volume[a2bbx6.iloc[(i,0)]], molar_volume[a2bbx6.iloc[(i,1)]], molar_volume[a2bbx6.iloc[(i,2)]], molar_volume[a2bbx6.iloc[(i,3)]]
data10.append(mv)
MV_a2bbx6=(np.array(data10))
data10=[]
for i in range (0, aabbx6_datasize):
mv=molar_volume[aabbx6.iloc[(i,0)]], molar_volume[aabbx6.iloc[(i,1)]], molar_volume[aabbx6.iloc[(i,2)]], molar_volume[aabbx6.iloc[(i,3)]], molar_volume[aabbx6.iloc[(i,4)]]
data10.append(mv)
MV_aabbx6=(np.array(data10))
# Average Ionic Radius
data11=[]
for i in range (0, abx3_datasize):
ir=average_ionic_radius[abx3.iloc[(i,0)]], average_ionic_radius[abx3.iloc[(i,1)]], average_ionic_radius[abx3.iloc[(i,2)]]
data11.append(ir)
IR_abx3=(np.array(data11))
data11=[]
for i in range (0, a2bbx6_datasize):
ir=average_ionic_radius[a2bbx6.iloc[(i,0)]], average_ionic_radius[a2bbx6.iloc[(i,1)]], average_ionic_radius[a2bbx6.iloc[(i,2)]], average_ionic_radius[a2bbx6.iloc[(i,3)]]
data11.append(ir)
IR_a2bbx6=(np.array(data11))
data11=[]
for i in range (0, aabbx6_datasize):
ir=(average_ionic_radius[aabbx6.iloc[(i,0)]], average_ionic_radius[aabbx6.iloc[(i,1)]], average_ionic_radius[aabbx6.iloc[(i,2)]],
average_ionic_radius[aabbx6.iloc[(i,3)]], average_ionic_radius[aabbx6.iloc[(i,4)]])
data11.append(ir)
IR_aabbx6=(np.array(data11))
# Polarizability
data12=[]
for i in range (0, abx3_datasize):
pz=polarizability[abx3.iloc[(i,0)]], polarizability[abx3.iloc[(i,1)]], polarizability[abx3.iloc[(i,2)]]
data12.append(pz)
PZ_abx3=(np.array(data12))
data12=[]
for i in range (0, a2bbx6_datasize):
pz=polarizability[a2bbx6.iloc[(i,0)]], polarizability[a2bbx6.iloc[(i,1)]], polarizability[a2bbx6.iloc[(i,2)]], polarizability[a2bbx6.iloc[(i,3)]]
data12.append(pz)
PZ_a2bbx6=(np.array(data12))
data12=[]
for i in range (0, aabbx6_datasize):
pz=polarizability[aabbx6.iloc[(i,0)]], polarizability[aabbx6.iloc[(i,1)]], polarizability[aabbx6.iloc[(i,2)]], polarizability[aabbx6.iloc[(i,3)]], polarizability[aabbx6.iloc[(i,4)]]
data12.append(pz)
PZ_aabbx6=(np.array(data12))
# Specific Heat
data13=[]
for i in range (0, abx3_datasize):
sh=specific_heat[abx3.iloc[(i,0)]], specific_heat[abx3.iloc[(i,1)]], specific_heat[abx3.iloc[(i,2)]]
data13.append(sh)
SH_abx3=(np.array(data13))
data13=[]
for i in range (0, a2bbx6_datasize):
sh=specific_heat[a2bbx6.iloc[(i,0)]], specific_heat[a2bbx6.iloc[(i,1)]], specific_heat[a2bbx6.iloc[(i,2)]], specific_heat[a2bbx6.iloc[(i,3)]]
data13.append(sh)
SH_a2bbx6=(np.array(data13))
data13=[]
for i in range (0, aabbx6_datasize):
sh=specific_heat[aabbx6.iloc[(i,0)]], specific_heat[aabbx6.iloc[(i,1)]], specific_heat[aabbx6.iloc[(i,2)]], specific_heat[aabbx6.iloc[(i,3)]], specific_heat[aabbx6.iloc[(i,4)]]
data13.append(sh)
SH_aabbx6=(np.array(data13))
# Thermal Conductivity
data14=[]
for i in range (0, abx3_datasize):
tc=thermal_conductivity[abx3.iloc[(i,0)]], thermal_conductivity[abx3.iloc[(i,1)]], thermal_conductivity[abx3.iloc[(i,2)]]
data14.append(tc)
TC_abx3=(np.array(data14))
data14=[]
for i in range (0, a2bbx6_datasize):
tc=thermal_conductivity[a2bbx6.iloc[(i,0)]], thermal_conductivity[a2bbx6.iloc[(i,1)]], thermal_conductivity[a2bbx6.iloc[(i,2)]], thermal_conductivity[a2bbx6.iloc[(i,3)]]
data14.append(tc)
TC_a2bbx6=(np.array(data14))
data14=[]
for i in range (0, aabbx6_datasize):
tc=(thermal_conductivity[aabbx6.iloc[(i,0)]], thermal_conductivity[aabbx6.iloc[(i,1)]], thermal_conductivity[aabbx6.iloc[(i,2)]],
thermal_conductivity[aabbx6.iloc[(i,3)]], thermal_conductivity[aabbx6.iloc[(i,4)]])
data14.append(tc)
TC_aabbx6=(np.array(data14))
#ABX3
# Atomic Number
Z1 = []
for value in Z_abx3[:,0]:
idx = [0 for _ in range(103)]
idx[value] = 1
Z1.append(idx)
Z1_abx3 = (np.array(Z1)).reshape(abx3_datasize,103,1)
Z2 = []
for value in Z_abx3[:,1]:
idx = [0 for _ in range(103)]
idx[value] = 1
Z2.append(idx)
Z2_abx3 = np.array(Z2).reshape(abx3_datasize,103,1)
Z3 = []
for value in Z_abx3[:,2]:
idx = [0 for _ in range(103)]
idx[value] = 1
Z3.append(idx)
Z3_abx3 = np.array(Z3).reshape(abx3_datasize,103,1)
zeropad = np.zeros((4200,103,4))
Z_abx3 = np.concatenate((Z1_abx3, Z2_abx3, Z3_abx3, zeropad), -1)
# Group Number
GN1 = []
for value in GN_abx3[:,0]:
idx = [0 for _ in range(18)]
idx[value] = 1
GN1.append(idx)
GN1_abx3 = np.array(GN1).reshape(abx3_datasize,18,1)
GN2 = []
for value in GN_abx3[:,1]:
idx = [0 for _ in range(18)]
idx[value] = 1
GN2.append(idx)
GN2_abx3 = np.array(GN2).reshape(abx3_datasize,18,1)
GN3 = []
for value in GN_abx3[:,2]:
idx = [0 for _ in range(18)]
idx[value] = 1
GN3.append(idx)
GN3_abx3 = np.array(GN3).reshape(abx3_datasize,18,1)
zeropad = np.zeros((4200,18,4))
GN_abx3 = np.concatenate((GN1_abx3, GN2_abx3, GN3_abx3, zeropad), -1)
# Row Number
RN1 = []
for value in RN_abx3[:,0]:
idx = [0 for _ in range(9)]
idx[value] = 1
RN1.append(idx)
RN1_abx3 = np.array(RN1).reshape(abx3_datasize,9,1)
RN2 = []
for value in RN_abx3[:,1]:
idx = [0 for _ in range(9)]
idx[value] = 1
RN2.append(idx)
RN2_abx3 = np.array(RN2).reshape(abx3_datasize,9,1)
RN3 = []
for value in RN_abx3[:,2]:
idx = [0 for _ in range(9)]
idx[value] = 1
RN3.append(idx)
RN3_abx3 = np.array(RN3).reshape(abx3_datasize,9,1)
zeropad = np.zeros((4200,9,4))
RN_abx3 = np.concatenate((RN1_abx3, RN2_abx3, RN3_abx3, zeropad), -1)
# Valence
VL1 = []
for value in VL_abx3[:,0]:
idx = [0 for _ in range(9)]
idx[value] = 1
VL1.append(idx)
VL1_abx3 = np.array(VL1).reshape(abx3_datasize,9,1)
VL2 = []
for value in VL_abx3[:,1]:
idx = [0 for _ in range(9)]
idx[value] = 1
VL2.append(idx)
VL2_abx3 = np.array(VL2).reshape(abx3_datasize,9,1)
VL3 = []
for value in VL_abx3[:,2]:
idx = [0 for _ in range(9)]
idx[value] = 1
VL3.append(idx)
VL3_abx3 = np.array(VL3).reshape(abx3_datasize,9,1)
zeropad = np.zeros((4200,9,4))
VL_abx3 = np.concatenate((VL1_abx3, VL2_abx3, VL3_abx3, zeropad), -1)
# Block
BK1 = []
for value in BK_abx3[:,0]:
idx = [0 for _ in range(4)]
idx[value] = 1
BK1.append(idx)
BK1_abx3 = np.array(BK1).reshape(abx3_datasize,4,1)
BK2 = []
for value in BK_abx3[:,1]:
idx = [0 for _ in range(4)]
idx[value] = 1
BK2.append(idx)
BK2_abx3 = np.array(BK2).reshape(abx3_datasize,4,1)
BK3 = []
for value in BK_abx3[:,2]:
idx = [0 for _ in range(4)]
idx[value] = 1
BK3.append(idx)
BK3_abx3 = np.array(BK3).reshape(abx3_datasize,4,1)
zeropad = np.zeros((4200,4,4))
BK_abx3 = np.concatenate((BK1_abx3, BK2_abx3, BK3_abx3, zeropad), -1)
# Electronegativity
X_=((X_abx3-0.7)/(3.98-0.7))*10 # max & min X are 3.98 & 0.7, respectively.
X_=(np.array(np.where(X_==10, 9, X_))).astype(int)
X1 = []
for value in X_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
X1.append(idx)
X1_abx3 = np.array(X1).reshape(abx3_datasize,10,1)
X2 = []
for value in X_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
X2.append(idx)
X2_abx3 = np.array(X2).reshape(abx3_datasize,10,1)
X3 = []
for value in X_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
X3.append(idx)
X3_abx3 = np.array(X3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
X_abx3 = np.concatenate((X1_abx3, X2_abx3, X3_abx3, zeropad), -1)
# Covalent Radius
CR_=((CR_abx3-0.28)/(2.6-0.28))*10 # max & min CR are 2.6 & 0.28, respectively.
CR_=(np.array(np.where(CR_==10, 9, CR_))).astype(int)
CR1 = []
for value in CR_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
CR1.append(idx)
CR1_abx3 = np.array(CR1).reshape(abx3_datasize,10,1)
CR2 = []
for value in CR_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
CR2.append(idx)
CR2_abx3 = np.array(CR2).reshape(abx3_datasize,10,1)
CR3 = []
for value in CR_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
CR3.append(idx)
CR3_abx3 = np.array(CR3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
CR_abx3 = np.concatenate((CR1_abx3, CR2_abx3, CR3_abx3, zeropad), -1)
# Electron Affinity
EA_=((EA_abx3-(-2.33))/(3.612724-(-2.33)))*10 # max & min EA are 3.612724 & -2.33, respectively.
EA_=(np.array(np.where(EA_==10, 9, EA_))).astype(int)
EA1 = []
for value in EA_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
EA1.append(idx)
EA1_abx3 = np.array(EA1).reshape(abx3_datasize,10,1)
EA2 = []
for value in EA_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
EA2.append(idx)
EA2_abx3 = np.array(EA2).reshape(abx3_datasize,10,1)
EA3 = []
for value in EA_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
EA3.append(idx)
EA3_abx3 = np.array(EA3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
EA_abx3 = np.concatenate((EA1_abx3, EA2_abx3, EA3_abx3, zeropad), -1)
# Average Ionic Radius
IR_=((IR_abx3-0)/(1.94-0))*10 # max & min IR are 1.94 & 0, respectively.
IR_=(np.array(np.where(IR_==10, 9, IR_))).astype(int)
IR1 = []
for value in IR_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
IR1.append(idx)
IR1_abx3 = np.array(IR1).reshape(abx3_datasize,10,1)
IR2 = []
for value in IR_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
IR2.append(idx)
IR2_abx3 = np.array(IR2).reshape(abx3_datasize,10,1)
IR3 = []
for value in IR_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
IR3.append(idx)
IR3_abx3 = np.array(IR3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
IR_abx3 = np.concatenate((IR1_abx3, IR2_abx3, IR3_abx3, zeropad), -1)
# Polarizability
PZ_=((PZ_abx3-0.204956)/(59.42-0.204956))*10 # max & min PZ are 59.42 & 0.204956, respectively.
PZ_=(np.array(np.where(PZ_==10, 9, PZ_))).astype(int)
PZ1 = []
for value in PZ_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
PZ1.append(idx)
PZ1_abx3 = np.array(PZ1).reshape(abx3_datasize,10,1)
PZ2 = []
for value in PZ_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
PZ2.append(idx)
PZ2_abx3 = np.array(PZ2).reshape(abx3_datasize,10,1)
PZ3 = []
for value in PZ_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
PZ3.append(idx)
PZ3_abx3 = np.array(PZ3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
PZ_abx3 = np.concatenate((PZ1_abx3, PZ2_abx3, PZ3_abx3, zeropad), -1)
# Molar Volume
MV_=(8.2752*np.log10(MV_abx3)-5.3165).astype(int) # Coverted to Log scale. Max & min MV are 70.94 & 4.39, respectively.
MV1 = []
for value in MV_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
MV1.append(idx)
MV1_abx3 = np.array(MV1).reshape(abx3_datasize,10,1)
MV2 = []
for value in MV_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
MV2.append(idx)
MV2_abx3 = np.array(MV2).reshape(abx3_datasize,10,1)
MV3 = []
for value in MV_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
MV3.append(idx)
MV3_abx3 = np.array(MV3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
MV_abx3 = np.concatenate((MV1_abx3, MV2_abx3, MV3_abx3, zeropad), -1)
# Ionization Energy
IE_=(12.49488*np.log10(IE_abx3)-7.37678).astype(int) # Coverted to Log scale. Max & min IE are 24.58741 & 3.8939, respectively.
IE1 = []
for value in IE_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
IE1.append(idx)
IE1_abx3 = np.array(IE1).reshape(abx3_datasize,10,1)
IE2 = []
for value in IE_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
IE2.append(idx)
IE2_abx3 = np.array(IE2).reshape(abx3_datasize,10,1)
IE3 = []
for value in IE_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
IE3.append(idx)
IE3_abx3 = np.array(IE3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
IE_abx3 = np.concatenate((IE1_abx3, IE2_abx3, IE3_abx3, zeropad), -1)
# Thermal Conductivity
TC_=(1.97007*np.log10(TC_abx3)+4.81188).astype(int) # Coverted to Log scale. Max & min TC are 430 & 0.00361, respectively.
TC1 = []
for value in TC_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
TC1.append(idx)
TC1_abx3 = np.array(TC1).reshape(abx3_datasize,10,1)
TC2 = []
for value in TC_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
TC2.append(idx)
TC2_abx3 = np.array(TC2).reshape(abx3_datasize,10,1)
TC3 = []
for value in TC_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
TC3.append(idx)
TC3_abx3 = np.array(TC3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
TC_abx3 = np.concatenate((TC1_abx3, TC2_abx3, TC3_abx3, zeropad), -1)
# Specific Heat
SH_=(4.24427*np.log10(SH_abx3)+5.0959).astype(int) # Coverted to Log scale. Max & min TC are 14.304 & 0.063, respectively.
SH1 = []
for value in SH_[:,0]:
idx = [0 for _ in range(10)]
idx[value] = 1
SH1.append(idx)
SH1_abx3 = np.array(SH1).reshape(abx3_datasize,10,1)
SH2 = []
for value in SH_[:,1]:
idx = [0 for _ in range(10)]
idx[value] = 1
SH2.append(idx)
SH2_abx3 = np.array(SH2).reshape(abx3_datasize,10,1)
SH3 = []
for value in SH_[:,2]:
idx = [0 for _ in range(10)]
idx[value] = 1
SH3.append(idx)
SH3_abx3 = np.array(SH3).reshape(abx3_datasize,10,1)
zeropad = np.zeros((4200,10,4))
SH_abx3 = np.concatenate((SH1_abx3, SH2_abx3, SH3_abx3, zeropad), -1)
#A2BBX6
# Atomic Number
Z1 = []
for value in Z_a2bbx6[:,0]:
idx = [0 for _ in range(103)]
idx[value] = 1
Z1.append(idx)
Z1_a2bbx6 = (np.array(Z1)).reshape(a2bbx6_datasize,103,1)
Z2 = []
for value in Z_a2bbx6[:,1]:
idx = [0 for _ in range(103)]
idx[value] = 1
Z2.append(idx)
Z2_a2bbx6 = np.array(Z2).reshape(a2bbx6_datasize,103,1)
Z3 = []
for value in Z_a2bbx6[:,2]:
idx = [0 for _ in range(103)]
idx[value] = 1
Z3.append(idx)
Z3_a2bbx6 = np.array(Z3).reshape(a2bbx6_datasize,103,1)
Z4 = []
for value in Z_a2bbx6[:,3]:
idx = [0 for _ in range(103)]
idx[value] = 1
Z4.append(idx)
Z4_a2bbx6 = np.array(Z4).reshape(a2bbx6_datasize,103,1)
zeropad = np.zeros((a2bbx6_datasize,103,3))
Z_a2bbx6 = np.concatenate((Z1_a2bbx6, Z2_a2bbx6, Z3_a2bbx6, Z4_a2bbx6, zeropad), -1)
# Group Number
GN1 = []
for value in GN_a2bbx6[:,0]:
idx = [0 for _ in range(18)]
idx[value] = 1
GN1.append(idx)
GN1_a2bbx6 = np.array(GN1).reshape(a2bbx6_datasize,18,1)
GN2 = []
for value in GN_a2bbx6[:,1]:
idx = [0 for _ in range(18)]
idx[value] = 1
GN2.append(idx)
GN2_a2bbx6 = np.array(GN2).reshape(a2bbx6_datasize,18,1)
GN3 = []
for value in GN_a2bbx6[:,2]:
idx = [0 for _ in range(18)]
idx[value] = 1
GN3.append(idx)
GN3_a2bbx6 = np.array(GN3).reshape(a2bbx6_datasize,18,1)
GN4 = []
for value in GN_a2bbx6[:,3]:
idx = [0 for _ in range(18)]
idx[value] = 1
GN4.append(idx)
GN4_a2bbx6 = np.array(GN4).reshape(a2bbx6_datasize,18,1)
zeropad = np.zeros((a2bbx6_datasize,18,3))
GN_a2bbx6 = np.concatenate((GN1_a2bbx6, GN2_a2bbx6, GN3_a2bbx6, GN4_a2bbx6, zeropad), -1)
# Row Number
RN1 = []
for value in RN_a2bbx6[:,0]:
idx = [0 for _ in range(9)]
idx[value] = 1
RN1.append(idx)
RN1_a2bbx6 = np.array(RN1).reshape(a2bbx6_datasize,9,1)
RN2 = []
for value in RN_a2bbx6[:,1]:
idx = [0 for _ in range(9)]
idx[value] = 1
RN2.append(idx)
RN2_a2bbx6 = np.array(RN2).reshape(a2bbx6_datasize,9,1)
RN3 = []
for value in RN_a2bbx6[:,2]:
idx = [0 for _ in range(9)]
idx[value] = 1
RN3.append(idx)
RN3_a2bbx6 = np.array(RN3).reshape(a2bbx6_datasize,9,1)
RN4 = []
for value in RN_a2bbx6[:,3]:
idx = [0 for _ in range(9)]
idx[value] = 1
RN4.append(idx)
RN4_a2bbx6 = np.array(RN4).reshape(a2bbx6_datasize,9,1)
zeropad = np.zeros((a2bbx6_datasize,9,3))
RN_a2bbx6 = np.concatenate((RN1_a2bbx6, RN2_a2bbx6, RN3_a2bbx6, RN4_a2bbx6, zeropad), -1)
# Valence
VL1 = []
for value in VL_a2bbx6[:,0]:
idx = [0 for _ in range(9)]
idx[value] = 1
VL1.append(idx)
VL1_a2bbx6 = np.array(VL1).reshape(a2bbx6_datasize,9,1)
VL2 = []
for value in VL_a2bbx6[:,1]:
idx = [0 for _ in range(9)]
idx[value] = 1
VL2.append(idx)
VL2_a2bbx6 = np.array(VL2).reshape(a2bbx6_datasize,9,1)
VL3 = []
for value in VL_a2bbx6[:,2]:
idx = [0 for _ in range(9)]
idx[value] = 1
VL3.append(idx)
VL3_a2bbx6 = np.array(VL3).reshape(a2bbx6_datasize,9,1)
VL4 = []
for value in VL_a2bbx6[:,3]:
idx = [0 for _ in range(9)]
idx[value] = 1
VL4.append(idx)
VL4_a2bbx6 = np.array(VL4).reshape(a2bbx6_datasize,9,1)
zeropad = np.zeros((a2bbx6_datasize,9,3))
VL_a2bbx6 = np.concatenate((VL1_a2bbx6, VL2_a2bbx6, VL3_a2bbx6, VL4_a2bbx6, zeropad), -1)