def testWithNumericalForceWithPBCEnabled(self):
atoms = Diamond(symbol="C", pbc=True)
calculator = torchani.models.ANI1x().ase()
atoms.set_calculator(calculator)
dyn = Langevin(atoms, 5 * units.fs, 30000000 * units.kB, 0.002)
dyn.run(100)
f = torch.from_numpy(atoms.get_forces())
fn = get_numeric_force(atoms, 0.001)
df = (f - fn).abs().max()
avgf = f.abs().mean()
if avgf > 0:
self.assertLess(df / avgf, 0.1)
def testWithNumericalForceWithPBCEnabled(self):
# Run a Langevin thermostat dynamic for 100 steps and after the dynamic
# check once that the numerical and analytical force agree to a given
# relative tolerance
atoms = Diamond(symbol="C", pbc=True)
calculator = self.model.ase()
atoms.set_calculator(calculator)
dyn = Langevin(atoms, 5 * units.fs, 30000000 * units.kB, 0.002)
dyn.run(100)
f = atoms.get_forces()
fn = get_numeric_force(atoms, 0.001)
self.assertEqual(f, fn, rtol=0.1, atol=0.1)
def _testForce(self, pbc):
atoms = Diamond(symbol="C", pbc=pbc)
builtin = torchani.neurochem.Builtins()
calculator = torchani.ase.Calculator(
builtin.species, builtin.aev_computer,
builtin.models, builtin.energy_shifter)
atoms.set_calculator(calculator)
dyn = Langevin(atoms, 5 * units.fs, 30000000 * units.kB, 0.002)
dyn.run(100)
f = torch.from_numpy(atoms.get_forces())
fn = get_numeric_force(atoms, 0.001)
df = (f - fn).abs().max()
avgf = f.abs().mean()
if avgf > 0:
self.assertLess(df / avgf, 0.1)
