diff --git a/pyproject.toml b/pyproject.toml
index 9f47f097..6c99bc27 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,7 +57,7 @@ orb = ["orb-models>=0.6.2"]
 sevenn = ["sevenn[torchsim]>=0.12.1"]
 nequip = ["nequip>=0.17.1"]
 nequix = ["nequix[torch-sim]>=0.4.5"]
-fairchem = ["fairchem-core>=2.7", "scipy<1.17.0"]
+fairchem = ["fairchem-core>=2.19.0", "scipy<1.17.0"]
 docs = [
     "autodoc_pydantic==2.2.0",
     "furo==2024.8.6",
diff --git a/tests/models/test_orb.py b/tests/models/test_orb.py
index de23cd17..1e7483cc 100644
--- a/tests/models/test_orb.py
+++ b/tests/models/test_orb.py
@@ -72,18 +72,11 @@ def orbv3_direct_20_omat_calculator() -> ORBCalculator:
     energy_atol=5e-5,
 )
 
-test_validate_conservative_model_outputs = pytest.mark.xfail(
-    reason=(
-        "Upstream ORB conservative model incorrectly squeezes length-1 batch "
-        "dimensions; see https://github.com/orbital-materials/orb-models/pull/158"
-    ),
-    strict=False,
-)(
-    make_validate_model_outputs_test(
-        model_fixture_name="orbv3_conservative_inf_omat_model",
-    )
+test_validate_conservative_model_outputs = make_validate_model_outputs_test(
+    model_fixture_name="orbv3_conservative_inf_omat_model",
 )
 
+
 test_validate_direct_model_outputs = pytest.mark.xfail(
     reason=(
         "Upstream ORB direct model shows batch-dependent leakage; "
diff --git a/torch_sim/models/orb.py b/torch_sim/models/orb.py
index 5c910cf4..e8736fe0 100644
--- a/torch_sim/models/orb.py
+++ b/torch_sim/models/orb.py
@@ -15,9 +15,11 @@
 
 
 try:
+    from orb_models.forcefield.inference.d3_model import D3SumModel
     from orb_models.forcefield.inference.orb_torchsim import OrbTorchSimModel
 
     import torch_sim as ts
+    from torch_sim.elastic import voigt_6_to_full_3x3_stress
 
     # Re-export with backward-compatible name
     class OrbModel(OrbTorchSimModel):
@@ -37,6 +39,48 @@ def _normalize_charge_spin(state: "ts.SimState") -> "ts.SimState":
                 spin=spin if spin is not None else zeros,
             )
 
+        def _get_results(self, out: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+            """Parses the results into a final output dictionary."""
+            results = {}
+            model = (
+                self.model.xc_model if isinstance(self.model, D3SumModel) else self.model
+            )
+            heads = getattr(model, "heads", {})
+            no_direct_energy_head = "energy" not in heads
+            no_direct_force_head = "forces" not in heads
+            no_direct_stress_head = "stress" not in heads
+            for prop in self.implemented_properties:
+                if prop == "free_energy" and no_direct_energy_head:
+                    continue
+                if prop == "forces" and no_direct_force_head:
+                    continue
+                if prop == "stress" and no_direct_stress_head:
+                    continue
+                _prop = "energy" if prop == "free_energy" else prop
+
+                # Do not squeeze the output tensors in the case of single atom cells
+                # TODO: remove after https://github.com/orbital-materials/orb-models/pull/158
+                results[prop] = torch.atleast_1d(out[_prop])
+
+            # Rename certain keys for the conservative model
+            if self.conservative:
+                if model.forces_name in results:
+                    results["direct_forces"] = results[model.forces_name]
+                results["forces"] = results[model.grad_forces_name]
+
+                if model.has_stress:
+                    if model.stress_name in results:
+                        results["direct_stress"] = results[model.stress_name]
+                    results["stress"] = results[model.grad_stress_name]
+
+            # Ensure stress has shape [-1, 3, 3]
+            if "stress" in results and results["stress"].shape[-1] == 6:
+                results["stress"] = voigt_6_to_full_3x3_stress(
+                    torch.atleast_2d(results["stress"])
+                )
+
+            return results
+
         def forward(self, *args: Any, **kwargs: Any) -> dict[str, Any]:
             """Run forward pass, detaching outputs unless retain_graph is True."""
             if args and isinstance(args[0], ts.SimState):
@@ -60,9 +104,13 @@ class OrbModel(ModelInterface):
         It raises an ImportError if accessed.
         """
 
-        def __init__(self, err: ImportError = exc, *_args: Any, **_kwargs: Any) -> None:
-            """Dummy init for type checking."""
-            raise err
+        # Capture the original ImportError in a closure-safe default so the
+        # fallback always re-raises the real import failure, even when callers
+        # pass positional/keyword args (e.g. ``OrbModel(orb_ff, adapter, ...)``)
+        # that would otherwise shadow an ``err`` parameter.
+        def __init__(self, *_args: Any, _err: ImportError = exc, **_kwargs: Any) -> None:
+            """Dummy init that re-raises the original import failure."""
+            raise _err
 
         def forward(self, *_args: Any, **_kwargs: Any) -> Any:
             """Unreachable — __init__ always raises."""