Feat: add vacancy formation task

lambench/metrics/downstream_tasks_metrics.yml

@@ -20,3 +20,7 @@ elastic:
   metrics: [MAE_G_VRH, MAE_K_VRH]
   penalty: success_rate
   dummy: {"MAE_G_VRH": 67.5431, "MAE_K_VRH": 136.2597}
+vacancy:
+  domain: Inorganic Materials
+  metrics: [MAE]
+  dummy: {"MAE": 4.381}

lambench/metrics/post_process.py

@@ -117,6 +117,7 @@ def process_domain_specific_for_one_model(model: BaseLargeAtomModel):
             "neb",
             "wiggle150",
             "elastic",
+            "vacancy",
         ]:
             applicability_results[record.task_name] = record.metrics
     return applicability_results

lambench/models/ase_models.py

@@ -258,6 +258,11 @@ def evaluate(
                 fmax = task.calculator_params.get("fmax", 1e-3)
                 max_steps = task.calculator_params.get("max_steps", 500)
                 return {"metrics": run_inference(self, task.test_data, fmax, max_steps)}
+            elif task.task_name == "vacancy":
+                from lambench.tasks.calculator.vacancy.vacancy import run_inference
+
+                assert task.test_data is not None
+                return {"metrics": run_inference(self, task.test_data)}
             else:
                 raise NotImplementedError(f"Task {task.task_name} is not implemented.")
 

lambench/tasks/calculator/calculator_tasks.yml

@@ -28,3 +28,6 @@ elastic:
   calculator_params:
     fmax: 0.001
     max_steps: 500
+vacancy:
+  test_data: /bohr/lambench-vacancy-a2xo/v1
+  calculator_params: null

lambench/tasks/calculator/vacancy/vacancy.py

@@ -0,0 +1,79 @@
+"""
+The test data is retrieved from:
+Chem. Mater. 2023, 35, 24, 10619–10634
+
+https://pubs.acs.org/doi/10.1021/acs.chemmater.3c02251
+
+Only 1813 structure pairs are used.
+
+"""
+
+from ase.io import read
+import numpy as np
+from ase import Atoms
+from tqdm import tqdm
+from pathlib import Path
+
+from sklearn.metrics import root_mean_squared_error, mean_absolute_error
+
+from lambench.models.ase_models import ASEModel
+import logging
+
+
+def get_oxygen_reference_energy(calc) -> float:
+    vacuum_size = 30  # Ångströms: Large cell size to ensure vacuum separation
+    o_o_bond_length = 1.23  # Ångströms: Experimental O-O bond length for O2
+    cell_vector = vacuum_size
+    cell = [cell_vector, cell_vector, cell_vector]
+    center = cell_vector / 2
+
+    positions = [
+        (center, center, center - o_o_bond_length / 2),
+        (center, center, center + o_o_bond_length / 2),
+    ]
+
+    molecular_oxygen = Atoms("O2", positions=positions, cell=cell, pbc=True)
+    molecular_oxygen.calc = calc
+    return molecular_oxygen.get_potential_energy() / 2
+
+
+def run_inference(
+    model: ASEModel,
+    test_data: Path,
+) -> dict[str, float]:
+    pristine_structures = read(test_data / "vacancy_pristine_structures.traj", ":")
+    defect_structures = read(test_data / "vacancy_defect_structures.traj", ":")
+    labels = np.load(test_data / "vacancy_evf_label.npy")
+
+    evf_lab = []
+    evf_pred = []
+    calc = model.calc
+
+    # Calculate reference energy for oxygen atom
+    E_o = get_oxygen_reference_energy(calc)
+
+    for pristine, defect, label in tqdm(
+        zip(pristine_structures, defect_structures, labels)
+    ):
+        natoms_pri = len(pristine)
+        natoms_def = len(defect)
+
+        n_oxygen = natoms_pri - natoms_def
+
+        pristine.calc = calc
+        defect.calc = calc
+        try:
+            final = defect.get_potential_energy()
+            initial = pristine.get_potential_energy()
+
+            e_vf = final + n_oxygen * E_o - initial
+            evf_lab.append(label)
+            evf_pred.append(e_vf)
+
+        except Exception as e:
+            logging.error(f"Error occurred while processing structures: {e}")
+
+    return {
+        "MAE": mean_absolute_error(evf_lab, evf_pred),  # eV
+        "RMSE": root_mean_squared_error(evf_lab, evf_pred),  # eV
+    }