const = neurochem.Constants(
os.path.join(model_path, 'rHCNOSFCl-5.1R_16-3.5A_a8-4.params'))
aev_computer = AEVComputer(Rcr=const.Rcr,
Rca=const.Rca,
EtaR=const.EtaR,
ShfR=const.ShfR,
EtaA=const.EtaA,
Zeta=const.Zeta,
ShfA=const.ShfA,
ShfZ=const.ShfZ,
num_species=const.num_species)
EShifter = neurochem.load_sae(os.path.join(model_path, 'sae_linfit.dat'))
ANI2_Network = neurochem.load_model(
const.species, os.path.join(model_path, r'train7\networks'))
class SpeciesEnergies(NamedTuple):
species: Tensor
energies: Tensor
def overforward(self,
species_aev: Tuple[Tensor, Tensor],
cell: Optional[Tensor] = None,
pbc: Optional[Tensor] = None) -> SpeciesEnergies:
species, aev = species_aev
species_ = species.flatten()
aev = aev.flatten(0, 1)
from torchani.neurochem import load_model
from torchani.ase import Calculator
from torchani import AEVComputer
from torchani.neurochem import load_sae
from torchani.nn import Sequential
import os
# A module defining a module needs to define only one variable,
# named `calculator`, which should be an instance of the ase.calculator.Calculator,
# a subclass of this, or a compatible class implementing the calculator interface.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_dir = os.path.dirname(os.path.realpath(__file__))
species_order = ['H', 'C', 'N', 'O']
const_file = os.path.join(model_dir,
'../ANI_common/rHCNO-5.2R_16-3.5A_a4-8.params')
consts = torchani.neurochem.Constants(const_file)
aev_computer = AEVComputer(**consts)
energy_shifter = load_sae(
os.path.join(model_dir, '../ANI_common/sae_linfit.dat'))
nn = load_model(consts.species, os.path.join(model_dir, '../ANI_common'))
nn.load_state_dict(
torch.load(os.path.join(model_dir, 'model1200EF_pre_1_best.pt')))
model_trained = Sequential(aev_computer, nn, energy_shifter).to(device)
calculator = Calculator(species=species_order, model=model_trained)
name = 'ANI_1200K'
import torchani
from torchani.neurochem import load_model
from torchani.ase import Calculator
from torchani import AEVComputer
from torchani.neurochem import load_sae
from torchani.nn import Sequential
import os
# A module defining a module needs to define only one variable,
# named `calculator`, which should be an instance of the ase.calculator.Calculator,
# a subclass of this, or a compatible class implementing the calculator interface.
model_dir = os.path.dirname(os.path.realpath(__file__))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
species_order = ['H', 'C', 'N', 'O']
const_file = os.path.join(model_dir,'rHCNO-5.2R_16-3.5A_a4-8.params')
consts = torchani.neurochem.Constants(const_file)
aev_computer = AEVComputer(**consts)
energy_shifter = load_sae(os.path.join(model_dir,'sae_linfit.dat'))
nn = load_model(consts.species, model_dir)
model_trained = Sequential(aev_computer, nn, energy_shifter).to(device)
calculator = Calculator(species=species_order, model=model_trained)
name = 'ANI'