Copy MLIAPUnified LJ example

examples/mliap/jax/mliap_unified_jax.py

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+from lammps.mliap.mliap_unified_abc import MLIAPUnified
+import numpy as np
+
+
+class MLIAPUnifiedJAX(MLIAPUnified):
+    """Test implementation for MLIAPUnified."""
+
+    def __init__(self, element_types, epsilon=1.0, sigma=1.0, rcutfac=1.25):
+        # ARGS: interface, element_types, ndescriptors, nparams, rcutfac
+        super().__init__(None, element_types, 1, 3, rcutfac)
+        # Mimicking the LJ pair-style:
+        # pair_style lj/cut 2.5
+        # pair_coeff * * 1 1
+        self.epsilon = epsilon
+        self.sigma = sigma
+
+    def compute_gradients(self, data):
+        """Test compute_gradients."""
+
+    def compute_descriptors(self, data):
+        """Test compute_descriptors."""
+
+    def compute_forces(self, data):
+        """Test compute_forces."""
+        eij, fij = self.compute_pair_ef(data)
+        data.update_pair_energy(eij)
+        data.update_pair_forces(fij)
+
+    def compute_pair_ef(self, data):
+        rij = data.rij
+
+        r2inv = 1.0 / np.sum(rij ** 2, axis=1)
+        r6inv = r2inv * r2inv * r2inv
+
+        lj1 = 4.0 * self.epsilon * self.sigma**12
+        lj2 = 4.0 * self.epsilon * self.sigma**6
+
+        eij = r6inv * (lj1 * r6inv - lj2)
+        fij = r6inv * (3.0 * lj2 - 6.0 * lj2 * r6inv) * r2inv
+        fij = fij[:, np.newaxis] * rij
+        return eij, fij