Add files via upload

Author: ss0832

multioptpy/Potential/keep_dihedral_angle_potential.py

@@ -114,15 +114,16 @@ def calc_energy(self, geom_num_list, bias_pot_params=[]):
         # Normalize vectors safely
         # We add a small epsilon to denominator to avoid NaN in the "linear" branch,
         # even though we will overwrite the result with 0 later.
-        n1_norm = torch.sqrt(n1_sq_norm)
-        n2_norm = torch.sqrt(n2_sq_norm)
+        n1_norm = torch.clamp(torch.sqrt(n1_sq_norm), min=1e-12)
+        n2_norm = torch.clamp(torch.sqrt(n2_sq_norm), min=1e-12)
 
-        n1_hat = n1 / (n1_norm.unsqueeze(-1) + 1e-12)
-        n2_hat = n2 / (n2_norm.unsqueeze(-1) + 1e-12)
+       
+        n1_hat = n1 / (n1_norm.unsqueeze(-1))
+        n2_hat = n2 / (n2_norm.unsqueeze(-1))
 
         # Normalize b2 to define the reference frame for sign
-        b2_norm = torch.linalg.norm(b2)
-        b2_hat = b2 / (b2_norm.unsqueeze(-1) + 1e-12)
+        b2_norm = torch.clamp(torch.linalg.norm(b2), min=1e-12)
+        b2_hat = b2 / (b2_norm.unsqueeze(-1))
 
         # ========================================
         # 4. Angle Calculation (atan2)
@@ -259,14 +260,14 @@ def get_indices(key):
         is_linear = (n1_sq_norm < self.COLLINEAR_CUT_SQ) | (n2_sq_norm < self.COLLINEAR_CUT_SQ)
 
         # Safe normalization
-        n1_norm = torch.sqrt(n1_sq_norm)
-        n2_norm = torch.sqrt(n2_sq_norm)
+        n1_norm = torch.clamp(torch.sqrt(n1_sq_norm), min=1e-12)
+        n2_norm = torch.clamp(torch.sqrt(n2_sq_norm), min=1e-12)
 
-        n1_hat = n1 / (n1_norm.unsqueeze(-1) + 1e-12)
-        n2_hat = n2 / (n2_norm.unsqueeze(-1) + 1e-12)
+        n1_hat = n1 / (n1_norm.unsqueeze(-1))
+        n2_hat = n2 / (n2_norm.unsqueeze(-1))
 
-        b2_norm = torch.linalg.norm(b2)
-        b2_hat = b2 / (b2_norm.unsqueeze(-1) + 1e-12)
+        b2_norm = torch.clamp(torch.linalg.norm(b2), min=1e-12)
+        b2_hat = b2 / (b2_norm.unsqueeze(-1))
 
         # ========================================
         # 4. Angle Calculation (atan2)

multioptpy/Potential/keep_outofplain_angle_potential.py

@@ -100,7 +100,8 @@ def calc_energy(self, geom_num_list, bias_pot_params=[]):
 
         # Safe normalization
         n_norm = torch.sqrt(n_sq_norm)
-        n_hat = n / (n_norm.unsqueeze(-1) + 1e-12)
+        n_hat_demon = torch.clamp(n_norm.unsqueeze(-1), min=1e-12)
+        n_hat = n / n_hat_demon
 
         # ========================================
         # 4. Angle Calculation (Robust atan2)
@@ -237,7 +238,8 @@ def get_indices(key):
 
         # Safe normalization
         n_norm = torch.sqrt(n_sq_norm)
-        n_hat = n / (n_norm.unsqueeze(-1) + 1e-12)
+        n_hat_demon = torch.clamp(n_norm.unsqueeze(-1), min=1e-12)
+        n_hat = n / n_hat_demon
 
         # ========================================
         # 4. Angle Calculation (Robust atan2)

multioptpy/Potential/keep_potential.py

@@ -50,11 +50,8 @@ def calc_energy(self, geom_num_list, bias_pot_params=[]):
         vec = geom_num_list[idx1] - geom_num_list[idx2]
 
         # 3. Robust Distance Calculation
-        # Standard torch.linalg.norm gradient is undefined at 0.
-        # Use sqrt(sum(x^2) + epsilon) for safety.
-        dist = torch.sqrt(torch.sum(vec**2))
-        if dist < 1e-12:
-            dist = dist + 1e-12
+     
+        dist = torch.clamp(torch.sqrt(torch.sum(vec**2)), min=1e-12)
 
         # 4. Energy Calculation
         # E = 0.5 * k * (r - r0)^2
@@ -111,9 +108,7 @@ def calc_energy(self, geom_num_list, bias_pot_params=[]):
 
         # 3. Robust Distance Calculation
         vec = fragm_1_center - fragm_2_center
-        distance = torch.sqrt(torch.sum(vec**2))
-        if distance < 1e-12:
-            distance = distance + 1e-12
+        distance = torch.clamp(torch.sqrt(torch.sum(vec**2)), min=1e-12)
 
         # 4. Energy
         energy = 0.5 * k * (distance - r0) ** 2