chore: format

Author: anyangml

lambench/metrics/downstream_tasks_metrics.yml

@@ -31,4 +31,4 @@ binding_energy:
 rxn_barrier:
   domain: Molecules
   metrics: [MAE]
-  dummy: {"MAE": 20.975}
+  dummy: {"MAE": 20.975}

lambench/models/ase_models.py

@@ -104,26 +104,23 @@ def calc(self, head=None) -> Calculator:
         else:
             self._calc = calculator_dispatch[self.model_family]()
         return self._calc
-    
+
     @calc.setter
     def calc(self, value: Calculator):
         logging.warning("Overriding the default calculator.")
         self._calc = value
 
     def _init_mace_calculator(self) -> Calculator:
         from mace.calculators import mace_mp
+
         if self.model_domain == "molecules":
             head = "omol"
         else:
             head = "oc20_usemppbe"
         return mace_mp(
-            model=self.model_path,
-            device="cuda",
-            default_dtype="float64",
-            head=head
+            model=self.model_path, device="cuda", default_dtype="float64", head=head
         )
 
-
     def _init_orb_calculator(self) -> Calculator:
         from orb_models.forcefield import pretrained
         from orb_models.forcefield.calculator import ORBCalculator

lambench/tasks/calculator/binding/binding.py

@@ -17,44 +17,44 @@
 import logging
 
 
-
-
 def run_inference(
     model: ASEModel,
     test_data: Path,
 ) -> dict[str, float]:
-
-    active_site_atoms = read(test_data / "active_site.traj",":")
-    drug_atoms = read(test_data / "drug.traj",":")
-    combined_atoms = read(test_data / "combined.traj",":")
+    active_site_atoms = read(test_data / "active_site.traj", ":")
+    drug_atoms = read(test_data / "drug.traj", ":")
+    combined_atoms = read(test_data / "combined.traj", ":")
     labels = np.load(test_data / "labels.npy")
 
     EV_TO_KCAL = 23.06092234465
 
-    calc =  model.calc
+    calc = model.calc
     preds = []
     success_labels = []
 
-    for site, drug, combo, label in tqdm(zip(active_site_atoms, drug_atoms, combined_atoms, labels)):
+    for site, drug, combo, label in tqdm(
+        zip(active_site_atoms, drug_atoms, combined_atoms, labels)
+    ):
         try:
             for atoms in (site, drug, combo):
                 atoms.calc = calc
-                atoms.info.update({"fparam": np.array([atoms.info["charge"], atoms.info["spin"]])})
+                atoms.info.update(
+                    {"fparam": np.array([atoms.info["charge"], atoms.info["spin"]])}
+                )
 
             site_energy = site.get_potential_energy()
             drug_energy = drug.get_potential_energy()
             combo_energy = combo.get_potential_energy()
 
-            binding_energy = combo_energy - site_energy - drug_energy 
+            binding_energy = combo_energy - site_energy - drug_energy
             preds.append(binding_energy * EV_TO_KCAL)
             success_labels.append(label)
         except Exception as e:
             logging.warning(f"Failed to calculate binding energy for one sample: {e}")
             continue
-    
 
     return {
         "MAE": mean_absolute_error(success_labels, preds),  # kcal/mol
         "RMSE": root_mean_squared_error(success_labels, preds),  # kcal/mol
         "success_rate": len(success_labels) / len(labels),
-    }
+    }

lambench/tasks/calculator/calculator_tasks.yml

@@ -36,4 +36,4 @@ binding_energy:
   calculator_params: null
 rxn_barrier:
   test_data: /bohr/lambench-BH876-uplk/v1/BH876
-  calculator_params: null
+  calculator_params: null

lambench/tasks/calculator/rxn_barrier/barrier.py

@@ -1,14 +1,14 @@
 """
 The test data is retrieved from:
 
-	@misc{liang2025gold,
-      title={Gold-Standard Chemical Database 137 (GSCDB137): A diverse set of accurate energy differences for assessing and developing density functionals}, 
+        @misc{liang2025gold,
+      title={Gold-Standard Chemical Database 137 (GSCDB137): A diverse set of accurate energy differences for assessing and developing density functionals},
       author={Jiashu Liang and Martin Head-Gordon},
       year={2025},
       eprint={2508.13468},
       archivePrefix={arXiv},
       primaryClass={physics.chem-ph},
-      url={https://arxiv.org/abs/2508.13468}, 
+      url={https://arxiv.org/abs/2508.13468},
 }
 
 https://github.com/JiashuLiang/GSCDB
@@ -27,13 +27,10 @@
 import logging
 
 
-
-
 def run_inference(
     model: ASEModel,
     test_data: Path,
 ) -> dict[str, float]:
-
     lookup_table = pd.read_csv(test_data / "lookup_table.csv")
     lookup_table.reset_index(inplace=True)
     stoichiometry = pd.read_csv(test_data / "stoichiometry.csv")
@@ -46,31 +43,36 @@ def run_inference(
     labels = []
     success = len(stoichiometry)
 
-    calc =  model.calc
+    calc = model.calc
 
     for i, row in tqdm(stoichiometry.iterrows()):
         try:
             reactions = row["Stoichiometry"].split(",")
-            num_species = len(reactions) // 2 
+            num_species = len(reactions) // 2
             pred = 0
             for i in range(num_species):
-                stoi = float(reactions[2*i])
-                reactant = reactions[2*i+1] 
-                structure_index = lookup_table[lookup_table["ID"] == reactant].index.values[0]
+                stoi = float(reactions[2 * i])
+                reactant = reactions[2 * i + 1]
+                structure_index = lookup_table[
+                    lookup_table["ID"] == reactant
+                ].index.values[0]
                 atoms = traj[structure_index]
-                atoms.info.update({"fparam": np.array([atoms.info["charge"], atoms.info["spin"]])})
+                atoms.info.update(
+                    {"fparam": np.array([atoms.info["charge"], atoms.info["spin"]])}
+                )
                 atoms.calc = calc
                 energy = atoms.get_potential_energy()
                 pred += stoi * energy
             preds.append(pred * EV_TO_KCAL)
             labels.append(row["Reference"] * HARTREE_TO_KCAL)
-        except:
-            logging.warning(f"Failed to calculate reaction energy for reaction: {row['Stoichiometry']}")
+        except Exception as e:
+            logging.warning(
+                f"Failed to calculate reaction energy for reaction: {row['Stoichiometry']}. Error: {e}"
+            )
             success -= 1
-    
 
     return {
         "MAE": mean_absolute_error(labels, preds),  # kcal/mol
         "RMSE": root_mean_squared_error(labels, preds),  # kcal/mol
         "success_rate": success / len(stoichiometry),
-    }
+    }