Use handwritten cosine similarity (#520)

torchani/aev.py

@@ -66,10 +66,9 @@ def angular_terms(Rca: float, ShfZ: Tensor, EtaA: Tensor, Zeta: Tensor,
     vectors12 = vectors12.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
     distances12 = vectors12.norm(2, dim=-5)
 
-    # 0.95 is multiplied to the cos values to prevent acos from
-    # returning NaN.
-    cos_angles = 0.95 * torch.nn.functional.cosine_similarity(vectors12[0], vectors12[1], dim=-5)
-    angles = torch.acos(cos_angles)
+    cos_angles = vectors12.prod(0).sum(1) / distances12.prod(0)
+    # 0.95 is multiplied to the cos values to prevent acos from returning NaN.
+    angles = torch.acos(0.95 * cos_angles)
 
     fcj12 = cutoff_cosine(distances12, Rca)
     factor1 = ((1 + torch.cos(angles - ShfZ)) / 2) ** Zeta