45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from .mliap_unified_abc import MLIAPUnified
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import numpy as np
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class MLIAPUnifiedLJ(MLIAPUnified):
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"""Test implementation for MLIAPUnified."""
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def __init__(self, element_types, epsilon=1.0, sigma=1.0, rcutfac=1.25):
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super().__init__()
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self.element_types = element_types
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self.ndescriptors = 1
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self.nparams = 3
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# Mimicking the LJ pair-style:
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# pair_style lj/cut 2.5
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# pair_coeff * * 1 1
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self.epsilon = epsilon
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self.sigma = sigma
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self.rcutfac = rcutfac
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def compute_gradients(self, data):
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"""Test compute_gradients."""
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def compute_descriptors(self, data):
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"""Test compute_descriptors."""
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def compute_forces(self, data):
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"""Test compute_forces."""
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eij, fij = self.compute_pair_ef(data)
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data.update_pair_energy(eij)
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data.update_pair_forces(fij)
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def compute_pair_ef(self, data):
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rij = data.rij
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r2inv = 1.0 / np.sum(rij ** 2, axis=1)
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r6inv = r2inv * r2inv * r2inv
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lj1 = 4.0 * self.epsilon * self.sigma**12
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lj2 = 4.0 * self.epsilon * self.sigma**6
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eij = r6inv * (lj1 * r6inv - lj2)
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fij = r6inv * (3.0 * lj2 - 6.0 * lj2 * r6inv) * r2inv
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fij = fij[:, np.newaxis] * rij
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return eij, fij
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