SCAN/descriptor.py at main · PEESEgroup/SCAN · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
import numpy as np
import pandas as pd

salt_properties = {

    'LiPF6': [0.22278622, 0.0498452, 0, 0, 0, 0, 0, 0.19672131, 0.22278622, 0.19047619, 0, 0, 0.19227575, 0.14135673],
    'LiBF4': [0.13748968, -0.21878225, 0, 0, 0, 0, 0, 0.13114754, 0.13748968, 0.14285714, 0, 0, 0.13501987, 0.11541728],
    'LiFSI': [0.27436924, -0.41373839, 0.78307985, 1, 0, 0, 0, 0.2295082, 0.27436924, 0.23809524, 0, 0.5, 0.23124724, 0.2139321],
    'LiTDI': [0.28163641, -0.28315789, 0.7088403, 0, 1, 0, 0, 0.2704918, 0.28163641, 0.33333333, 0, 0.375, 0.30185877, 0.3454273],
    'LiTFSI': [0.42105325, 0.00917183, 0.67984791, 1, 0, 0, 0, 0.37704918, 0.42105325, 0.38095238, 0, 0.5, 0.40738047, 0.38403373],
    'LiClO4': [0.15603528, -1, 0.87680608, 0, 0, 0, 0, 0.13114754, 0.15603528, 0.14285714, 0, 0.5, 0.13501987, 0.11541728],
    'LiAsF6': [0.28724189, -0.11037152, 0, 0, 0, 0, 0, 0.19672131, 0.28724189, 0.19047619, 0, 0, 0.19227575, 0.14135673],
    'LiBMB': [0.32536266, -0.59259546, 1, 0, 0, 0, 0, 0.32786885, 0.31944919, 0.38095238, 0, 1, 0.33161006, 0.40336038],
    'LiBOB': [0.28421769, -0.69324045, 1, 0, 0, 0, 0, 0.27868852, 0.28421769, 0.33333333, 0, 1, 0.28917741, 0.34759499],
    'LiBPFPB': [1, 0.5125387, 0.35095057, 0, 0, 0, 0, 1, 1, 1, 0, 0.5, 1, 1],
    'LiCF3SO3': [0.22880969, -0.37985036, 0.54372624, 0, 0, 0, 0, 0.20491803, 0.22880969, 0.21428571, 0, 0.375, 0.21003091, 0.18755309],
    'LiPDI': [0.34627687, 0.23312178, 0.72490494, 0.5, 1, 1, 0, 0.3442623, 0.34479851, 0.38095238, 1, 0.375, 0.37686981, 0.4175631],
    'LiN(CF3SO2)2': [0.42105325, -0.2498323, 0.78307985, 1, 0, 0, 0, 0.37704918, 0.42105325, 0.38095238, 0, 0.5, 0.38126932, 0.3582037]
}


solvent_properties = {
    'EC': [0.25594508, 0.03998747, 0.76988082, 0, 0, 0, 0, 0.27868852, 0.24859623, 0.3, 0, 0.6, 0.27124761, 0.33566168],
    'PC': [0.29671342, 0.14139173, 0.76988082, 0, 0, 0, 0, 0.32786885, 0.28412984, 0.35, 0, 0.6, 0.32482532, 0.38134457],
    'DMC': [0.26180442, 0.10419712, 0.76988082, 0, 0, 0, 0, 0.29508197, 0.24859623, 0.3, 0, 0.6, 0.30731233, 0.32571118],
    'EMC': [0.30257276, 0.206019, 0.76988082, 0.08333333, 0, 0, 0, 0.3442623, 0.28412984, 0.35, 0, 0.6, 0.35084631, 0.38370694],
    'DEC': [0.3433411, 0.30784089, 0.76988082, 0.16666667, 0, 0, 0, 0.39344262, 0.31966345, 0.4, 0, 0.6, 0.39438029, 0.44170269],
    'DME': [0.2619323, 0.07287534, 0.4, 0.25, 0, 0, 0, 0.31147541, 0.23679804, 0.3, 0, 0.4, 0.2972686, 0.33802404],
    'DMSO': [0.22709596, -0.00138338, 0.36988082, 0, 0, 0, 0, 0.21311475, 0.21326675, 0.2, 0, 0.2, 0.22024438, 0.2009032],
    'AN': [0.11931722, 0.13830654, 0.51549296, 0, 0, 0, 0, 0.13114754, 0.11250584, 0.15, 0, 0.2, 0.16666667, 0.16403677],
    'MOEMC': [0.3898409, 0.10852996, 0.96988082, 0.25, 0, 0, 0, 0.44262295, 0.36699525, 0.45, 0, 0.8, 0.43791427, 0.49969845],
    'TFP': [1, 1, 0.96988082, 0.5, 0, 0, 0, 1, 1, 1, 0, 0.8, 1, 1],
    'EA': [0.25607296, 0.14862184, 0.56988082, 0.08333333, 0, 0, 0, 0.29508197, 0.23679804, 0.3, 0, 0.4, 0.30731233, 0.32130294],
    'MA': [0.21530462, 0.04679996, 0.56988082, 0, 0, 0, 0, 0.24590164, 0.20126443, 0.25, 0, 0.4, 0.26377836, 0.26330719],
    'FEC': [0.30823156, 0.11714345, 0.76988082, 0, 0, 0, 0, 0.32786885, 0.30480034, 0.35, 0, 0.6, 0.32482532, 0.38134457],
    'DOL': [0.21530462, -0.00242744, 0.4, 0, 0, 0, 0, 0.24590164, 0.20126443, 0.25, 0, 0.4, 0.2176699, 0.28997879],
    '2-MeTHF': [0.2503415, 0.30938087, 0.2, 0, 0, 0, 0, 0.29508197, 0.22499985, 0.3, 0, 0.2, 0.27124761, 0.33566168],
    'DMM': [0.47150547, 0.2800167, 0.6, 0.5, 0, 0, 0, 0.55737705, 0.42626428, 0.55, 0, 0.6, 0.53502596, 0.6033771],
    'Freon 11': [0.39924898, 0.59605346, 0, 0, 0, 0, 0, 0.26229508, 0.40639256, 0.25, 0, 0, 0.27704855, 0.23198303],
    'Methylene chloride': [0.24685089, 0.37103257, 0, 0, 0, 0, 0, 0.16393443, 0.2453035, 0.15, 0, 0, 0.16666667, 0.16403677],
    'THF': [0.20957316, 0.20797661, 0.2, 0, 0, 0, 0, 0.24590164, 0.18946624, 0.25, 0, 0.2, 0.2176699, 0.28997879],
    'Toluene': [0.26780036, 0.5207298, 0, 0, 1, 0, 0, 0.29508197, 0.24873527, 0.35, 0, 0, 0.31478158, 0.39365743],
    'Sulfolane': [0.3492731, 0.05089789, 0.73976165, 0, 0, 0, 0, 0.3442623, 0.33166577, 0.35, 0, 0.4, 0.32805178, 0.37199717],
    '2-Glyme': [0.38996879, 0.07720819, 0.6, 0.5, 0, 0, 0, 0.45901639, 0.35519706, 0.45, 0, 0.6, 0.42787054, 0.51201131],
    '3-Glyme': [0.51800527, 0.08154103, 0.8, 0.75, 0, 0, 0, 0.60655738, 0.47359608, 0.6, 0, 0.8, 0.55847248, 0.68599859],
    '4-Glyme': [0.64604175, 0.08587388, 1, 1, 0, 0, 0, 0.75409836, 0.5919951, 0.75, 0, 1, 0.68907442, 0.85998586],
    '3-Me-2-Oxazolidinone': [0.2938535, 0.01785341, 0.64008667, 0, 0, 0, 0, 0.32786885, 0.27823666, 0.35, 0, 0.4, 0.32482532, 0.38329749],
    '3-MeSulfolane': [0.39004145, 0.11510754, 0.73976165, 0, 0, 0, 0, 0.39344262, 0.36719938, 0.4, 0, 0.4, 0.38162949, 0.41768006],
    'Ethyldiglyme': [0.38996879, 0.00830027, 0.8383532, 0.5, 0, 1, 0, 0.45901639, 0.35519706, 0.45, 1, 0.6, 0.42787054, 0.51201131],
    'DMF': [0.21244471, -0.07715598, 0.44008667, 0.08333333, 0, 0, 0, 0.24590164, 0.19537125, 0.25, 0, 0.2, 0.26377836, 0.26330719],
    'Ethylbenzene': [0.3085687, 0.58702234, 0, 0.08333333, 1, 0, 0, 0.3442623, 0.28426889, 0.4, 0, 0, 0.35831556, 0.45606143],
    'Ethylmonoglyme': [0.22116396, -0.09785446, 0.6383532, 0.16666667, 0, 1, 0, 0.26229508, 0.20126443, 0.25, 1, 0.4, 0.25373462, 0.28002829],
    'Benzene': [0.22703202, 0.4402276, 0, 0, 1, 0, 0, 0.24590164, 0.21320166, 0.3, 0, 0, 0.26120387, 0.34797454],
    'g-Butyrolactone': [0.25021362, 0.08441219, 0.56988082, 0, 0, 0, 0, 0.27868852, 0.23679804, 0.3, 0, 0.4, 0.27124761, 0.33566168],
    'Cumene': [0.34933705, 0.73345166, 0, 0.08333333, 1, 0, 0, 0.39344262, 0.3198025, 0.45, 0, 0, 0.41189327, 0.499289],
    'Propylsulfone': [0.43666913, 0.31875131, 0.73976165, 0.33333333, 0, 0, 0, 0.45901639, 0.40273299, 0.45, 0, 0.4, 0.45118446, 0.47803819],
    'Pseudocumeme': [0.34933705, 0.68173418, 0, 0, 1, 0, 0, 0.39344262, 0.3198025, 0.45, 0, 0, 0.421937, 0.48697614],
    'TEOS': [0.60549429, 0.40927125, 0.8, 0.66666667, 0, 0, 0, 0.6557377, 0.55668633, 0.65, 0, 0.8, 0.62532038, 0.72409335],
    'm-Xylene': [0.3085687, 0.60123199, 0, 0, 1, 0, 0, 0.3442623, 0.28426889, 0.4, 0, 0, 0.36835929, 0.43934033],
    'o-Xylene': [0.3085687, 0.60123199, 0, 0, 1, 0, 0, 0.3442623, 0.28426889, 0.4, 0, 0, 0.36835929, 0.44129325]
}

c_unit_encode = {
    'mol/kg': [0],
    'mol/l': [1]
}

solvent_ratio_type_code = {
    'mol': [1, 0, 0],
    'w': [0, 1, 0],
    'v': [0, 0, 1]
}


if __name__ == "__main__":
    df = pd.read_csv("data/calisol.csv")
    generated_features = []

    salt_features = []
    solvent_features = []
    condition_features = []
    conductivity_target = []

    for _, row in df.iterrows():
        # Salt feature
        lithium_salt = row['salt']
        salt_vector = salt_properties[lithium_salt]
        salt_features.append(salt_vector)

        # Solvent feature
        solvent_vector = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        for solvent, proportion in row[6:].items():
            if proportion == 0:
                continue
            else:
                # print(solvent)
                solvent_vector = [solvent_vector[j] + [i*proportion for i in solvent_properties[solvent]][j] for j in range(14)]
        solvent_features.append(solvent_vector)

        condition_vector = []
        condition_vector.append(row['T'])
        condition_vector += c_unit_encode[row['c units']]
        condition_vector += solvent_ratio_type_code[row['solvent ratio type']]
        condition_vector.append(row['c'])
        condition_features.append(condition_vector)

        conductivity_target.append(row['k'])

    salt_features = np.array(salt_features)
    solvent_features = np.array(solvent_features)
    condition_features = np.array(condition_features)
    conductivity_target = np.array(conductivity_target)

    np.save("salt_features.npy", salt_features)
    np.save("solvent_features.npy", solvent_features)
    np.save("condition_features.npy", condition_features)

    np.savetxt("conductivity_target.txt", conductivity_target)