diff --git a/lambench/metrics/vishelper/metrics_calculations.py b/lambench/metrics/vishelper/metrics_calculations.py
index 2c8e515..ddea233 100644
--- a/lambench/metrics/vishelper/metrics_calculations.py
+++ b/lambench/metrics/vishelper/metrics_calculations.py
@@ -159,7 +159,9 @@ def calculate_generalizability_downstream_score(self) -> tuple[dict, dict]:
         )  # drop models with missing domain results
 
         # # Now aggregate all domains to get the final generalizability metrics for each model
-        return domain_results.to_dict(orient="index"), domain_results.mean(axis=1).to_dict()
+        return domain_results.to_dict(orient="index"), domain_results.mean(
+            axis=1
+        ).to_dict()
 
     def calculate_stability_results(self) -> dict[str, float]:
         """This calculates the stability score for a given LAM."""
diff --git a/lambench/tasks/calculator/elastic/elastic.py b/lambench/tasks/calculator/elastic/elastic.py
index be4f01e..e0c0c51 100644
--- a/lambench/tasks/calculator/elastic/elastic.py
+++ b/lambench/tasks/calculator/elastic/elastic.py
@@ -102,9 +102,10 @@ def get_elastic_for_one(
     """
     atoms = read(StringIO(atom_info["poscar"]), format="vasp")
 
+    calc = model.calc
     relaxed_atoms = model.run_ase_relaxation(
         atoms=atoms,
-        calc=model.calc,
+        calc=calc,
         fmax=fmax,
         steps=max_steps,
         fix_symmetry=False,
@@ -119,7 +120,7 @@ def get_elastic_for_one(
     stresses = []
     for deformed_structure in deformed_structure_set:
         atoms = deformed_structure.to_ase_atoms()
-        atoms.calc = model.calc
+        atoms.calc = calc
         stresses.append(atoms.get_stress(voigt=False))
 
     strains = [
diff --git a/lambench/tasks/calculator/torsionnet/torsionnet.py b/lambench/tasks/calculator/torsionnet/torsionnet.py
index 4d11de9..3a91eda 100644
--- a/lambench/tasks/calculator/torsionnet/torsionnet.py
+++ b/lambench/tasks/calculator/torsionnet/torsionnet.py
@@ -30,6 +30,8 @@ def run_torsionnet(
     result = {}  # predicted energy
     label = {}  # label energy
 
+    calc = model.calc
+
     # 500 fragments from Torsionnet500 dataset
     for fragment in tqdm(test_data.iterdir()):
         if not fragment.is_dir():
@@ -40,7 +42,7 @@ def run_torsionnet(
         for frame in dpdata.LabeledSystem(file_name=fragment, fmt="deepmd/raw"):
             assert len(frame) == 1
             atoms: Atoms = frame.to_ase_structure()[0]  # type: ignore
-            atoms.calc = model.calc
+            atoms.calc = calc
             pred_energy = atoms.get_potential_energy() * 23.0609  # eV to kcal/mol
             result[fragment.name].append(pred_energy)
             label_energy = frame.data["energies"][0]