diff --git a/tests/models/test_mattersim.py b/tests/models/test_mattersim.py
index 91cd1ee8c..ee495aa7c 100644
--- a/tests/models/test_mattersim.py
+++ b/tests/models/test_mattersim.py
@@ -1,5 +1,3 @@
-# codespell-ignore: convertor
-
 import traceback
 
 import ase.spacegroup
diff --git a/torch_sim/models/fairchem.py b/torch_sim/models/fairchem.py
index 32d30f558..cdc7fb246 100644
--- a/torch_sim/models/fairchem.py
+++ b/torch_sim/models/fairchem.py
@@ -254,4 +254,4 @@ def forward(self, state: SimState, **_kwargs: object) -> dict[str, torch.Tensor]
                 stress = stress.view(-1, 3, 3)
             results["stress"] = stress
 
-        return results
+        return {k: v.detach() for k, v in results.items()}
diff --git a/torch_sim/models/fairchem_legacy.py b/torch_sim/models/fairchem_legacy.py
index a006cb201..05e452f1b 100644
--- a/torch_sim/models/fairchem_legacy.py
+++ b/torch_sim/models/fairchem_legacy.py
@@ -425,10 +425,10 @@ def forward(  # noqa: C901
         ):
             data_list.append(
                 Data(
-                    pos=sim_state.positions[c - n : c].clone(),
-                    cell=sim_state.row_vector_cell[idx, None].clone(),
-                    atomic_numbers=sim_state.atomic_numbers[c - n : c].clone(),
-                    fixed=fixed[c - n : c].clone(),
+                    pos=sim_state.positions[c - n : c].detach().clone(),
+                    cell=sim_state.row_vector_cell[idx, None].detach().clone(),
+                    atomic_numbers=sim_state.atomic_numbers[c - n : c].detach().clone(),
+                    fixed=fixed[c - n : c].detach().clone(),
                     natoms=n,
                     pbc=sim_state.pbc,
                 )
@@ -449,9 +449,9 @@ def forward(  # noqa: C901
             _pred = predictions[key]
             if key in self._reshaped_props:
                 _pred = _pred.reshape(self._reshaped_props.get(key)).squeeze()
-            results[key] = _pred.detach()
+            results[key] = _pred
 
         results["energy"] = results["energy"].squeeze(dim=1)
         if results.get("stress") is not None and len(results["stress"].shape) == 2:
             results["stress"] = results["stress"].unsqueeze(dim=0)
-        return results
+        return {k: v.detach() for k, v in results.items()}
diff --git a/torch_sim/models/graphpes_framework.py b/torch_sim/models/graphpes_framework.py
index 0bca0cdd5..ab8af45d0 100644
--- a/torch_sim/models/graphpes_framework.py
+++ b/torch_sim/models/graphpes_framework.py
@@ -185,4 +185,5 @@ def forward(self, state: ts.SimState, **_kwargs: object) -> dict[str, torch.Tens
         if not isinstance(cutoff, torch.Tensor):
             raise TypeError("GraphPES model cutoff must be a tensor")
         atomic_graph = state_to_atomic_graph(state, cutoff)
-        return self._gp_model.predict(atomic_graph, self._properties)  # type: ignore[return-value]
+        preds = self._gp_model.predict(atomic_graph, self._properties)  # ty: ignore[call-non-callable]
+        return {k: v.detach() for k, v in preds.items()}
diff --git a/torch_sim/models/metatomic.py b/torch_sim/models/metatomic.py
index e4ed32a35..758c50d6b 100644
--- a/torch_sim/models/metatomic.py
+++ b/torch_sim/models/metatomic.py
@@ -237,7 +237,7 @@ def forward(  # noqa: C901, PLR0915
         )
 
         results: dict[str, torch.Tensor] = {}
-        results["energy"] = model_outputs["energy"].block().values.detach().squeeze(-1)
+        results["energy"] = model_outputs["energy"].block().values.squeeze(-1)
 
         # Compute forces and/or stresses if requested
         tensors_for_autograd = []
@@ -286,4 +286,4 @@ def forward(  # noqa: C901, PLR0915
             else:
                 results["stress"] = torch.empty_like(cell)
 
-        return results
+        return {k: v.detach() for k, v in results.items()}
diff --git a/torch_sim/models/orb.py b/torch_sim/models/orb.py
index a02025048..9e9d333b8 100644
--- a/torch_sim/models/orb.py
+++ b/torch_sim/models/orb.py
@@ -466,4 +466,4 @@ def forward(self, state: SimState, **_kwargs: object) -> dict[str, torch.Tensor]
                 torch.atleast_2d(results["stress"])
             )
 
-        return results
+        return {k: v.detach() for k, v in results.items()}