File size: 4,606 Bytes
4947683 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | import chemparse
import numpy as np
import torch
from torch.utils.data import Dataset
from torch_geometric.data import Data
chemical_symbols = [
# 0
'X',
# 1
'H', 'He',
# 2
'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne',
# 3
'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'Ar',
# 4
'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn',
'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr',
# 5
'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd',
'In', 'Sn', 'Sb', 'Te', 'I', 'Xe',
# 6
'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy',
'Ho', 'Er', 'Tm', 'Yb', 'Lu',
'Hf', 'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb', 'Bi',
'Po', 'At', 'Rn',
# 7
'Fr', 'Ra', 'Ac', 'Th', 'Pa', 'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk',
'Cf', 'Es', 'Fm', 'Md', 'No', 'Lr',
'Rf', 'Db', 'Sg', 'Bh', 'Hs', 'Mt', 'Ds', 'Rg', 'Cn', 'Nh', 'Fl', 'Mc',
'Lv', 'Ts', 'Og']
class SampleDataset(Dataset):
def __init__(self, formula, num_evals):
super().__init__()
self.formula = formula
self.num_evals = num_evals
self.get_structure()
def get_structure(self):
self.composition = chemparse.parse_formula(self.formula)
chem_list = []
for elem in self.composition:
num_int = int(self.composition[elem])
chem_list.extend([chemical_symbols.index(elem)] * num_int)
self.chem_list = chem_list
def __len__(self) -> int:
return self.num_evals
def __getitem__(self, index):
return Data(
atom_types=torch.LongTensor(self.chem_list),
num_atoms=len(self.chem_list),
num_nodes=len(self.chem_list),
)
train_dist = {
'perov_5': [0, 0, 0, 0, 0, 1],
'carbon_24': [0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.3250697750779839,
0.0,
0.27795107535708424,
0.0,
0.15383352487276308,
0.0,
0.11246100804465604,
0.0,
0.04958134953209654,
0.0,
0.038745690362830404,
0.0,
0.019044491873255624,
0.0,
0.010178952552946971,
0.0,
0.007059596125430964,
0.0,
0.006074536200952225],
'perov': [0, 0, 0, 0, 0, 1],
'carbon': [0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.3250697750779839,
0.0,
0.27795107535708424,
0.0,
0.15383352487276308,
0.0,
0.11246100804465604,
0.0,
0.04958134953209654,
0.0,
0.038745690362830404,
0.0,
0.019044491873255624,
0.0,
0.010178952552946971,
0.0,
0.007059596125430964,
0.0,
0.006074536200952225],
'mp_20' : [0.0,
0.0021742334905660377,
0.021079009433962265,
0.019826061320754717,
0.15271226415094338,
0.047132959905660375,
0.08464770047169812,
0.021079009433962265,
0.07808814858490566,
0.03434551886792453,
0.0972877358490566,
0.013303360849056603,
0.09669811320754718,
0.02155807783018868,
0.06522700471698113,
0.014372051886792452,
0.06703272405660378,
0.00972877358490566,
0.053176591981132074,
0.010576356132075472,
0.08995430424528301]
}
class GenDataset(Dataset):
def __init__(self, dataset, total_num):
super().__init__()
self.total_num = total_num
self.distribution = train_dist[dataset]
self.num_atoms = np.random.choice(len(self.distribution), total_num, p = self.distribution)
self.is_carbon = dataset == 'carbon_24'
def __len__(self) -> int:
return self.total_num
def __getitem__(self, index):
num_atom = self.num_atoms[index]
data = Data(
num_atoms=torch.LongTensor([num_atom]),
num_nodes=num_atom,
)
if self.is_carbon:
data.atom_types = torch.LongTensor([6] * num_atom)
return data
|