def loss(flatten_params, initial_state, target_energy):
state = scf.kohn_sham_iteration(
state=initial_state,
num_electrons=self.num_electrons,
xc_energy_density_fn=tree_util.Partial(
xc_energy_density_fn,
params=np_utils.unflatten(spec, flatten_params)),
interaction_fn=utils.exponential_coulomb,
enforce_reflection_symmetry=True)
return (state.total_energy - target_energy)**2
def loss(flatten_params, initial_state, target_density):
state = scf.kohn_sham_iteration(
state=initial_state,
num_electrons=self.num_electrons,
xc_energy_density_fn=tree_util.Partial(
xc_energy_density_fn,
params=np_utils.unflatten(spec, flatten_params)),
interaction_fn=utils.exponential_coulomb,
enforce_reflection_symmetry=False)
return jnp.sum(jnp.abs(state.density -
target_density)) * utils.get_dx(self.grids)
def loss(flatten_params, target_energy):
state = scf.kohn_sham(locations=self.locations,
nuclear_charges=self.nuclear_charges,
num_electrons=self.num_electrons,
num_iterations=3,
grids=self.grids,
xc_energy_density_fn=tree_util.Partial(
xc_energy_density_fn,
params=np_utils.unflatten(
spec, flatten_params)),
interaction_fn=utils.exponential_coulomb)
final_state = scf.get_final_state(state)
return (final_state.total_energy - target_energy)**2
def loss(flatten_params, target_density):
state = scf.kohn_sham(locations=self.locations,
nuclear_charges=self.nuclear_charges,
num_electrons=self.num_electrons,
num_iterations=3,
grids=self.grids,
xc_energy_density_fn=tree_util.Partial(
xc_energy_density_fn,
params=np_utils.unflatten(
spec, flatten_params)),
interaction_fn=utils.exponential_coulomb,
density_mse_converge_tolerance=-1)
final_state = scf.get_final_state(state)
return jnp.sum(jnp.abs(final_state.density -
target_density)) * utils.get_dx(self.grids)
def test_flatten(self):
(tree, shapes), vec = np_utils.flatten(
[(jnp.array([1, 2, 3]), (jnp.array([4, 5]))), jnp.array([99])])
self.assertIsInstance(vec, np.ndarray)
np.testing.assert_allclose(vec, [1., 2., 3., 4., 5., 99.])
self.assertEqual(shapes, [(3,), (2,), (1,)])
# unflatten should convert 1d array back to pytree.
params = np_utils.unflatten((tree, shapes), vec)
self.assertIsInstance(params[0][0], np.ndarray)
np.testing.assert_allclose(params[0][0], [1., 2., 3.])
self.assertIsInstance(params[0][1], np.ndarray)
np.testing.assert_allclose(params[0][1], [4., 5.])
self.assertIsInstance(params[1], np.ndarray)
np.testing.assert_allclose(params[1], [99.])