def QM9datasets(root_dir=default_qm9_dir):
root_dir = os.path.expanduser(root_dir)
filename = f"{root_dir}data.pz"
if os.path.exists(filename):
return torch.load(filename)
else:
datasets, num_species, charge_scale = initialize_datasets(
(-1, -1, -1),
"data",
'qm9',
subtract_thermo=True,
force_download=True)
qm9_to_eV = {
'U0': 27.2114,
'U': 27.2114,
'G': 27.2114,
'H': 27.2114,
'zpve': 27211.4,
'gap': 27.2114,
'h**o': 27.2114,
'lumo': 27.2114
}
for dataset in datasets.values():
dataset.convert_units(qm9_to_eV)
dataset.num_species = 5
dataset.charge_scale = 9
os.makedirs(root_dir, exist_ok=True)
torch.save((datasets, num_species, charge_scale), filename)
return (datasets, num_species, charge_scale)
def MD17datasets(root_dir=default_md17_dir, task='benzene'):
root_dir = os.path.expanduser(root_dir)
filename = f"{root_dir}data.pz"
if os.path.exists(filename):
return torch.load(filename)
else:
datasets, num_species, charge_scale = initialize_datasets(
(-1, -1, -1), "data", 'md17', subset=task, force_download=True)
mean_energy = datasets['train'].data['energies'].mean()
for dataset in datasets.values():
dataset.data['energies'] -= mean_energy
os.makedirs(root_dir, exist_ok=True)
torch.save((datasets, num_species, charge_scale), filename)
return (datasets, num_species, charge_scale)