diff --git a/torch_sim/runners.py b/torch_sim/runners.py
index 5dbd0f39..a49ba07d 100644
--- a/torch_sim/runners.py
+++ b/torch_sim/runners.py
@@ -748,22 +748,7 @@ def static(
     state: SimState = ts.initialize_state(system, model.device, model.dtype)
 
     batch_iterator = _configure_batches_iterator(state, model, autobatcher=autobatcher)
-    properties = ["potential_energy"]
-    if model.compute_forces:
-        properties.append("forces")
-    if model.compute_stress:
-        properties.append("stress")
-    if isinstance(trajectory_reporter, dict):
-        trajectory_reporter = copy.deepcopy(trajectory_reporter)
-        trajectory_reporter["state_kwargs"] = {
-            "variable_atomic_numbers": True,
-            "variable_masses": True,
-            "save_forces": model.compute_forces,
-        }
-    trajectory_reporter = _configure_reporter(
-        trajectory_reporter or dict(filenames=None),
-        properties=properties,
-    )
+    trajectory_reporter, _ = _configure_static_reporter(trajectory_reporter, model)
 
     @dataclass(kw_only=True)
     class StaticState(SimState):
@@ -778,7 +763,7 @@ class StaticState(SimState):
         }
 
     all_props: list[dict[str, torch.Tensor]] = []
-    og_filenames = trajectory_reporter.filenames
+    og_filenames = trajectory_reporter.filenames if trajectory_reporter else None
 
     tqdm_pbar = None
     if pbar and autobatcher:
@@ -812,13 +797,17 @@ class StaticState(SimState):
             ),
         )
 
-        props = trajectory_reporter.report(static_state, 0, model=model)
-        all_props.extend(props)
+        if trajectory_reporter:
+            props = trajectory_reporter.report(static_state, 0, model=model)
+            all_props.extend(props)
+        else:
+            all_props.extend(_collect_base_properties(sub_state, static_state, model))
 
         if tqdm_pbar:
             tqdm_pbar.update(static_state.n_systems)
 
-    trajectory_reporter.finish()
+    if trajectory_reporter:
+        trajectory_reporter.finish()
 
     if isinstance(batch_iterator, BinningAutoBatcher):
         # reorder properties to match original order of states
@@ -827,3 +816,65 @@ class StaticState(SimState):
         return [prop for _, prop in sorted(indexed_props, key=lambda x: x[0])]
 
     return all_props
+
+
+def _configure_static_reporter(
+    trajectory_reporter: TrajectoryReporter | dict | None,
+    model: ModelInterface,
+) -> tuple[TrajectoryReporter | None, list[str]]:
+    """Configure trajectory reporter for static calculations.
+
+    Args:
+        trajectory_reporter: Optional reporter or dict config
+        model: The model interface to check which properties are computed
+
+    Returns:
+        Tuple of (configured reporter or None, list of properties)
+    """
+    properties = ["potential_energy"]
+    if model.compute_forces:
+        properties.append("forces")
+    if model.compute_stress:
+        properties.append("stress")
+
+    if trajectory_reporter is None:
+        return None, properties
+
+    if isinstance(trajectory_reporter, dict):
+        trajectory_reporter = copy.deepcopy(trajectory_reporter)
+        trajectory_reporter["state_kwargs"] = {
+            "variable_atomic_numbers": True,
+            "variable_masses": True,
+            "save_forces": model.compute_forces,
+        }
+
+    return _configure_reporter(trajectory_reporter, properties=properties), properties
+
+
+def _collect_base_properties(
+    sub_state: SimState,
+    static_state: SimState,
+    model: ModelInterface,
+) -> list[dict[str, torch.Tensor]]:
+    """Collect base properties for each system when no trajectory reporter is used.
+
+    Args:
+        sub_state: The sub-state being processed
+        static_state: State containing energy, forces, and stress from model
+        model: The model interface to check which properties are computed
+
+    Returns:
+        List of property dictionaries, one per system
+    """
+    props_list: list[dict[str, torch.Tensor]] = []
+    for sys_idx in range(sub_state.n_systems):
+        atom_mask = sub_state.system_idx == sys_idx
+        base_props: dict[str, torch.Tensor] = {
+            "potential_energy": static_state.energy[sys_idx],
+        }
+        if model.compute_forces:
+            base_props["forces"] = static_state.forces[atom_mask]
+        if model.compute_stress:
+            base_props["stress"] = static_state.stress[sys_idx]
+        props_list.append(base_props)
+    return props_list