def output_fn(array):
output_array = utils.flip_and_average(locations=locations,
grids=grids,
array=array)
return utils.flip_and_average(locations=locations,
grids=grids,
array=fn(output_array))
def test_flip_and_average_location_not_on_grids(self):
with self.assertRaisesRegex(ValueError,
r'Location 0\.25 is not on the grids'):
utils.flip_and_average(
# 0.25 is not on the grids.
locations=jnp.array([0.0, 0.25]),
grids=jnp.array([-0.1, 0.0, 0.1, 0.2, 0.3]),
# Values of array do not matter in this test.
array=jnp.array([0.1, 0.2, 0.6, 0.7, 0.2]))
def test_flip_and_average_the_back_of_array_center_not_on_grids(self):
np.testing.assert_allclose(
utils.flip_and_average(
locations=jnp.array([0.4, 0.5]),
grids=jnp.array([-0.2, -0.1, 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
array=jnp.array([0.1, 0.2, 0.6, 0.7, 0.2, 0.3, 0.5, 0.1, 0.8])),
# The center is 0.45, which is the grid point between index 6 and 7.
# The array on the grids [0.3, 0.4, 0.5, 0.6] are flipped:
# [0.3, 0.5, 0.1, 0.8]
# -> [0.8, 0.1, 0.5, 0.3]
# The averaged array is
# [0.55, 0.3, 0.3, 0.55]
# Replace the corresponding range (slice(5, 9)) in the original array:
# [0.1, 0.2, 0.6, 0.7, 0.2, 0.3, 0.5, 0.1, 0.8]
# -> [0.1, 0.2, 0.6, 0.7, 0.2, 0.55, 0.3, 0.3, 0.55]
[0.1, 0.2, 0.6, 0.7, 0.2, 0.55, 0.3, 0.3, 0.55])
def test_flip_and_average_the_front_of_array_center_on_grids(self):
np.testing.assert_allclose(
utils.flip_and_average(
locations=jnp.array([-0.1, 0.3]),
grids=jnp.array([-0.2, -0.1, 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
array=jnp.array([0.1, 0.2, 0.6, 0.7, 0.2, 0.3, 0.5, 0.1, 0.8])),
# The center is 0.1, which is the grid point with index 3.
# The array on the grids [-0.2, -0.1, 0., 0.1, 0.2, 0.3, 0.4]
# are flipped:
# [0.1, 0.2, 0.6, 0.7, 0.2, 0.3, 0.5]
# -> [0.5, 0.3, 0.2, 0.7, 0.6, 0.2, 0.1]
# The averaged array is
# [0.3, 0.25, 0.4, 0.7, 0.4, 0.25, 0.3]
# Replace the corresponding range (slice(0, 7)) in the original array:
# [0.1, 0.2, 0.6, 0.7, 0.2, 0.3, 0.5, 0.1, 0.8]
# -> [0.3, 0.25, 0.4, 0.7, 0.4, 0.25, 0.3, 0.1, 0.8]
[0.3, 0.25, 0.4, 0.7, 0.4, 0.25, 0.3, 0.1, 0.8])
def kohn_sham_iteration(state, num_electrons, xc_energy_density_fn,
interaction_fn, enforce_reflection_symmetry):
"""One iteration of Kohn-Sham calculation.
Note xc_energy_density_fn must be wrapped by jax.tree_util.Partial so this
function can take a callable. When the arguments of this callable changes,
e.g. the parameters of the neural network, kohn_sham_iteration() will not be
recompiled.
Args:
state: KohnShamState.
num_electrons: Integer, the number of electrons in the system. The first
num_electrons states are occupid.
xc_energy_density_fn: function takes density (num_grids,) and returns
the energy density (num_grids,).
interaction_fn: function takes displacements and returns
float numpy array with the same shape of displacements.
enforce_reflection_symmetry: Boolean, whether to enforce reflection
symmetry. If True, the system are symmetric respecting to the center.
Returns:
KohnShamState, the next state of Kohn-Sham iteration.
"""
if enforce_reflection_symmetry:
xc_energy_density_fn = _flip_and_average_fn(xc_energy_density_fn,
locations=state.locations,
grids=state.grids)
hartree_potential = get_hartree_potential(density=state.density,
grids=state.grids,
interaction_fn=interaction_fn)
xc_potential = get_xc_potential(density=state.density,
xc_energy_density_fn=xc_energy_density_fn,
grids=state.grids)
ks_potential = hartree_potential + xc_potential + state.external_potential
xc_energy_density = xc_energy_density_fn(state.density)
# Solve Kohn-Sham equation.
density, total_eigen_energies, gap = solve_noninteracting_system(
external_potential=ks_potential,
num_electrons=num_electrons,
grids=state.grids)
total_energy = (
# kinetic energy = total_eigen_energies - external_potential_energy
total_eigen_energies -
get_external_potential_energy(external_potential=ks_potential,
density=density,
grids=state.grids)
# Hartree energy
+ get_hartree_energy(
density=density, grids=state.grids, interaction_fn=interaction_fn)
# xc energy
+ get_xc_energy(density=density,
xc_energy_density_fn=xc_energy_density_fn,
grids=state.grids)
# external energy
+ get_external_potential_energy(
external_potential=state.external_potential,
density=density,
grids=state.grids))
if enforce_reflection_symmetry:
density = utils.flip_and_average(locations=state.locations,
grids=state.grids,
array=density)
return state._replace(density=density,
total_energy=total_energy,
hartree_potential=hartree_potential,
xc_potential=xc_potential,
xc_energy_density=xc_energy_density,
gap=gap)
