def load_lammps_eam_parameters(
file: TextIO
) -> Tuple[Callable[[Array], Array], Callable[[Array], Array], Callable[
[Array], Array], float]:
"""Reads EAM parameters from a LAMMPS file and returns relevant spline fits.
This function reads single-element EAM potential fit parameters from a file
in DYNAMO funcl format. In summary, the file contains:
Line 1-3: comments,
Line 4: Number of elements and the element type,
Line 5: The number of charge values that the embedding energy is evaluated
on (num_drho), interval between the charge values (drho), the number of
distances the pairwise energy and the charge density is evaluated on (num_dr),
the interval between these distances (dr), and the cutoff distance (cutoff).
The lines that come after are the embedding function evaluated on num_drho
charge values, charge function evaluated at num_dr distance values, and
pairwise energy evaluated at num_dr distance values. Note that the pairwise
energy is multiplied by distance (in units of eV x Angstroms). For more
details of the DYNAMO file format, see:
https://sites.google.com/a/ncsu.edu/cjobrien/tutorials-and-guides/eam
Args:
f: File handle for the EAM parameters text file.
Returns:
charge_fn: A function that takes an ndarray of shape [n, m] of distances
between particles and returns a matrix of charge contributions.
embedding_fn: Function that takes an ndarray of shape [n] of charges and
returns an ndarray of shape [n] of the energy cost of embedding an atom
into the charge.
pairwise_fn: A function that takes an ndarray of shape [n, m] of distances
and returns an ndarray of shape [n, m] of pairwise energies.
cutoff: Cutoff distance for the embedding_fn and pairwise_fn.
"""
raw_text = file.read().split('\n')
if 'setfl' not in raw_text[0]:
raise ValueError(
'File format is incorrect, expected LAMMPS setfl format.')
temp_params = raw_text[4].split()
num_drho, num_dr = int(temp_params[0]), int(temp_params[2])
drho, dr, cutoff = float(temp_params[1]), float(temp_params[3]), float(
temp_params[4])
data = maybe_downcast([float(i) for i in raw_text[6:-1]])
embedding_fn = interpolate.spline(data[:num_drho], drho)
charge_fn = interpolate.spline(data[num_drho:num_drho + num_dr], dr)
# LAMMPS EAM parameters file lists pairwise energies after multiplying by
# distance, in units of eV*Angstrom. We divide the energy by distance below,
distances = np.arange(num_dr) * dr
# Prevent dividing by zero at zero distance, which will not
# affect the calculation
distances = np.where(distances == 0, f32(0.001), distances)
pairwise_fn = interpolate.spline(
data[num_dr + num_drho:num_drho + 2 * num_dr] / distances, dr)
return charge_fn, embedding_fn, pairwise_fn, cutoff
def make_eam_test_splines():
cutoff = 6.28721
num_spline_points = 21
dr = np.arange(0, num_spline_points) * (cutoff / num_spline_points)
dr = np.array(dr, f32)
drho = np.arange(0, 2, 2. / num_spline_points)
drho = np.array(drho, f32)
density_data = np.array([
2.78589606e-01, 2.02694937e-01, 1.45334053e-01, 1.06069912e-01,
8.42517168e-02, 7.65140344e-02, 7.76263116e-02, 8.23214224e-02,
8.53322309e-02, 8.13915861e-02, 6.59095390e-02, 4.28915711e-02,
2.27910928e-02, 1.13713167e-02, 6.05020311e-03, 3.65836583e-03,
2.60587564e-03, 2.06750708e-03, 1.48749693e-03, 7.40019174e-04,
6.21225205e-05
], np.float64)
embedding_data = np.array([
1.04222211e-10, -1.04142633e+00, -1.60359806e+00, -1.89287637e+00,
-2.09490167e+00, -2.26456628e+00, -2.40590322e+00, -2.52245359e+00,
-2.61385603e+00, -2.67744693e+00, -2.71053295e+00, -2.71110418e+00,
-2.69287013e+00, -2.68464527e+00, -2.69204083e+00, -2.68976209e+00,
-2.66001244e+00, -2.60122024e+00, -2.51338548e+00, -2.39650817e+00,
-2.25058831e+00
], np.float64)
pairwise_data = np.array([
6.27032242e+01, 3.49638589e+01, 1.79007014e+01, 8.69001383e+00,
4.51545250e+00, 2.83260884e+00, 1.93216616e+00, 1.06795515e+00,
3.37740836e-01, 1.61087890e-02, -6.20816372e-02, -6.51314297e-02,
-5.35210341e-02, -5.20950200e-02, -5.51709524e-02, -4.89093894e-02,
-3.28051688e-02, -1.13738785e-02, 2.33833655e-03, 4.19132033e-03,
1.68600692e-04
], np.float64)
charge_fn = spline(density_data, dr[1] - dr[0])
embedding_fn = spline(embedding_data, drho[1] - drho[0])
pairwise_fn = spline(pairwise_data, dr[1] - dr[0])
return charge_fn, embedding_fn, pairwise_fn
-2.61385603e+00, -2.67744693e+00, -2.71053295e+00, -2.71110418e+00,
-2.69287013e+00, -2.68464527e+00, -2.69204083e+00, -2.68976209e+00,
-2.66001244e+00, -2.60122024e+00, -2.51338548e+00, -2.39650817e+00,
-2.25058831e+00
], np.float32)
PAIRWISE_DATA = np.array([
6.27032242e+01, 3.49638589e+01, 1.79007014e+01, 8.69001383e+00,
4.51545250e+00, 2.83260884e+00, 1.93216616e+00, 1.06795515e+00,
3.37740836e-01, 1.61087890e-02, -6.20816372e-02, -6.51314297e-02,
-5.35210341e-02, -5.20950200e-02, -5.51709524e-02, -4.89093894e-02,
-3.28051688e-02, -1.13738785e-02, 2.33833655e-03, 4.19132033e-03,
1.68600692e-04
], np.float32)
charge_fn = spline(DENSITY_DATA, dr[1] - dr[0])
embedding_fn = spline(EMBEDDING_DATA, drho[1] - drho[0])
pairwise_fn = spline(PAIRWISE_DATA, dr[1] - dr[0])
class EnergyTest(jtu.JaxTestCase):
# pylint: disable=g-complex-comprehension
@parameterized.named_parameters(
jtu.cases_from_list({
'testcase_name':
'_dim={}_alpha={}_dtype={}'.format(dim, alpha, dtype.__name__),
'spatial_dimension':
dim,
'alpha':
alpha,
