materialsproject
diff --git a/‎.github/workflows/testing.yml‎
Lines changed: 8 additions & 2 deletions b/‎.github/workflows/testing.yml‎
Lines changed: 8 additions & 2 deletions
diff --git a/‎.gitignore‎
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diff --git a/‎.mypy_cache/3.12/cache.db‎ b/‎.mypy_cache/3.12/cache.db‎
diff --git a/‎pyproject.toml‎
Lines changed: 11 additions & 18 deletions b/‎pyproject.toml‎
Lines changed: 11 additions & 18 deletions
diff --git a/‎src/atomate2/common/jobs/electrode.py‎
Lines changed: 1 addition & 3 deletions b/‎src/atomate2/common/jobs/electrode.py‎
Lines changed: 1 addition & 3 deletions
diff --git a/‎src/atomate2/forcefields/utils.py‎
Lines changed: 22 additions & 58 deletions b/‎src/atomate2/forcefields/utils.py‎
Lines changed: 22 additions & 58 deletions
diff --git a/‎src/atomate2/torchsim/core.py‎
Lines changed: 5 additions & 4 deletions b/‎src/atomate2/torchsim/core.py‎
Lines changed: 5 additions & 4 deletions
diff --git a/‎tests/forcefields/flows/test_approx_neb.py‎
Lines changed: 5 additions & 8 deletions b/‎tests/forcefields/flows/test_approx_neb.py‎
Lines changed: 5 additions & 8 deletions
diff --git a/‎tests/forcefields/flows/test_gruneisen.py‎
Lines changed: 7 additions & 0 deletions b/‎tests/forcefields/flows/test_gruneisen.py‎
Lines changed: 7 additions & 0 deletions
diff --git a/‎tests/forcefields/flows/test_phonon.py‎
Lines changed: 10 additions & 6 deletions b/‎tests/forcefields/flows/test_phonon.py‎
Lines changed: 10 additions & 6 deletions
@@ -38,9 +38,10 @@ jobs:
       run:
         shell: bash -l {0} # enables conda/mamba env activation by reading bash profile
     strategy:
+      fail-fast: false
       matrix:
         python-version: ["3.12"]
-        dep-group: ["generic", "torch-limited", "e3nn-limited"]
+        dep-group: ["generic", "torch-limited", "e3nn-limited", "numpy-limited"]
 
     steps:
       - name: Check out repo
@@ -79,9 +80,13 @@ jobs:
         env:
           MP_API_KEY: ${{ secrets.MP_API_KEY }}
 
+        # torch-limited loads the heaviest MLIP stack (matgl + nequip + torch);
+        # running it single-process avoids the OOM that kills the runner when
+        # xdist spawns multiple workers each holding a copy of the models.
         run: |
           micromamba activate a2
-          pytest -n auto --cov=atomate2 --cov-report=xml tests/forcefields
+          pytest -n ${{ matrix.dep-group == 'torch-limited' && '1' || 'auto' }} --cov=atomate2 --cov-report=xml tests/forcefields
+          #pytest -n auto --cov=atomate2 --cov-report=xml tests/forcefields
 
       - name: Forcefield tutorial
         if: matrix.dep-group == 'torch-limited'
@@ -113,6 +118,7 @@ jobs:
       run:
         shell: bash -l {0} # enables conda/mamba env activation by reading bash profile
     strategy:
+      fail-fast: false
       matrix:
         python-version: ["3.11", "3.12"]
         split: [1, 2, 3]
 
@@ -29,7 +29,6 @@ develop-eggs
 .installed.cfg
 lib
 lib64
-uv.lock
 
 # Installer logs
 pip-log.txt
 
@@ -47,7 +47,10 @@ aims = ["pymatgen-io-aims>=0.0.5", "pymatgen>=2025.10.7"]
 amset = ["amset>=0.4.15", "pydash"]
 cclib = ["cclib>=1.8.1"]
 mp = ["mp-api>=0.37.5"]
-phonons = ["phonopy>=2.43.6", "seekpath>=2.0.0"]
+# phonopy 4.x changed force-constants/primitive-axis handling, breaking the
+# phonon.save -> phonopy.load round-trip used by the Grüneisen workflow
+# ("Force constants shape disagrees with crystal structure setting").
+phonons = ["phonopy>=2.43.6,<4", "seekpath>=2.0.0"]
 lobster = ["ijson>=3.2.2", "lobsterpy>=0.6.0"]
 defects = [
     "dscribe>=1.2.0",
@@ -60,7 +63,7 @@ ase-ext = ["tblite>=0.3.0; platform_system=='Linux'"]
 forcefields-demo = ["chgnet>=0.3.8","atomate2[ase]"]
 
 torchsim = [
-    "torch-sim-atomistic==0.5.0; python_version >= '3.12'"
+    "torch-sim-atomistic==0.6.0; python_version >= '3.12'"
 ]
 jdftx = ["pymatgen==2026.5.4"]
 approxneb = ["pymatgen-analysis-diffusion>=2024.7.15"]
@@ -95,31 +98,21 @@ strict-forcefields-generic = [
     "deepmd-kit==3.1.3",
     "tensorflow-cpu==2.21.0; sys_platform == 'linux'",
     "tensorflow==2.21.0; sys_platform == 'darwin' or sys_platform == 'win32'",
-    "mattersim==1.2.3",
-    "wandb>=0.24.0", # required for mattersim
-    "upet>=0.2.1",
+#     "mattersim>=1.2.3", # need to be activated again
+    "wandb==0.24.0", # required for mattersim
+    "upet==0.2.5",
 ]
 strict-forcefields-torch-limited = [
-    "matgl==3.0.2",
-    # Note that DGL stopped building new releases for Mac + Windows after 2.2.0.
-    # That enforces a simultaneous pin on torch / torchdata
-    # Linux users can acces newer versions of dgl / torch / torchdata via conda.
-    # Mac / Windows users will need to install from source
-    "dgl==2.2.0; sys_platform == 'darwin' or sys_platform == 'win32'",
-    "dgl<=2.4.0; sys_platform == 'linux'",
-    "torch==2.11.0; sys_platform == 'darwin' or sys_platform == 'win32'",
-    "torch==2.2.0; sys_platform == 'linux'",
-    "torchdata==0.7.1",
+    "matgl==4.0.2",
     "nequip==0.17.1", # requires numpy<2 because of matscipy
-    "numpy==1.26.4",
 ]
+
 strict-forcefields-e3nn-limited = [
-    "mace-torch==0.3.16",
+    "mace-torch==0.3.15",
     "torch-dftd==0.5.3",
 ]
 strict-forcefields-numpy-limited = [
     "nequip-allegro==0.8.2",
-    "numpy==1.26.4",
 ]
 
 strict = [
 
@@ -202,9 +202,7 @@ def get_insertion_electrode_doc(
             ient.data["material_id"] = AlphaID(int(ULID.from_str(ient.entry_id)))
         else:
             ient.data["material_id"] = ient.entry_id
-    return InsertionElectrodeDoc.from_entries(
-        computed_entries, working_ion_entry, battery_id=None
-    )
+    return InsertionElectrodeDoc.from_entries(computed_entries, working_ion_entry)
 
 
 @job
 
@@ -304,7 +304,10 @@ def ase_calculator(
 
     if (
         isinstance(calculator_meta, str)
-        and (calculator_meta in map(str, MLFF) or calculator_meta in MLFF)
+        and (
+            calculator_meta in map(str, MLFF)
+            or calculator_meta in {m.value for m in MLFF}
+        )
     ) or isinstance(calculator_meta, MLFF):
         calculator_name = MLFF(calculator_meta)
 
@@ -349,23 +352,22 @@ def ase_calculator(
                 )
 
                 import matgl
-
-                # matgl >= 3.0 dropped the legacy MP-2021.2.8 / MPtrj weights and
-                # the GitHub `pretrained_models/` fallback. All pre-trained
-                # weights now live on the `materialyze` HF org with the
-                # ``<Architecture>-PES-<Dataset>-<Func>-<Version>`` naming.
-                # See https://huggingface.co/materialyze for the canonical list.
+                from matgl.ext.ase import PESCalculator
+
+                # matgl >= 4.0 removed the DGL backend; matgl now targets
+                # PyTorch Geometric exclusively and all potentials load through
+                # the single ``matgl.ext.ase.PESCalculator``. Pre-trained weights
+                # use the ``<Architecture>-PES-<Dataset>-<Func>-<Version>`` naming
+                # and live on the ``materialyze`` HF org (resolved from bare names
+                # by ``load_model``), except the CHGNet PyG weights, hosted under
+                # ``BowenD-UCB``. See https://huggingface.co/materialyze.
                 match calculator_name:
                     case MLFF.M3GNet:
-                        # Only the MatPES-trained PyG M3GNet potential is
-                        # distributed on HF for matgl 3.x.
                         path = kwargs.get("path", "M3GNet-PES-MatPES-PBE-2025.2")
-                        backend = "PYG"
                     case MLFF.CHGNet:
-                        # The MatPES-trained CHGNet weights remain DGL-backed.
-                        path = kwargs.get("path", "CHGNet-PES-MatPES-PBE-2025.2.10")
-                        backend = "DGL"
-
+                        path = kwargs.get(
+                            "path", "BowenD-UCB/CHGNet-PyG-MatPES-PBE-2025.2.10"
+                        )
                     case MLFF.MATPES_R2SCAN | MLFF.MATPES_PBE:
                         # ``calculator_name.value`` is e.g. "MatPES-PBE";
                         # take the suffix to construct the HF repo name.
@@ -376,20 +378,11 @@ def ase_calculator(
                             "path",
                             f"{architecture}-PES-MatPES-{functional}-{version}",
                         )
-                        backend = "PYG"
-
-                _set_matgl_backend(backend)
 
                 if default_dtype is not None:
                     matgl.set_default_dtype(default_dtype)
 
-                matgl_calc = getattr(
-                    import_module(f"matgl.ext._ase_{matgl.config.BACKEND.lower()}"),
-                    "PESCalculator",
-                    None,
-                )
-
-                calculator = matgl_calc(matgl.load_model(path), **kwargs)
+                calculator = PESCalculator(matgl.load_model(path), **kwargs)
 
             case MLFF.MACE | MLFF.MACE_MP_0 | MLFF.MACE_MPA_0 | MLFF.MACE_MP_0B3:
                 from mace.calculators import MACECalculator, mace_mp
@@ -440,9 +433,11 @@ def ase_calculator(
 
                 calculator = getattr(
                     NequIPCalculator,
-                    "from_compiled_model"
-                    if hasattr(NequIPCalculator, "from_compiled_model")
-                    else "from_deployed_model",
+                    (
+                        "from_compiled_model"
+                        if hasattr(NequIPCalculator, "from_compiled_model")
+                        else "from_deployed_model"
+                    ),
                 )(**kwargs)
 
             case MLFF.FAIRChem:
@@ -492,37 +487,6 @@ def _load_calc_cls(
     return MontyDecoder().process_decoded(calculator_meta)
 
 
-def _set_matgl_backend(backend: str) -> None:
-    """Switch the active matgl backend, reloading dependent submodules.
-
-    matgl reads ``matgl.config.BACKEND`` once when ``matgl.models`` /
-    ``matgl.apps.pes`` are first imported, and the conditional imports inside
-    those packages decide which backend-specific classes (e.g.
-    ``matgl.models.CHGNet``) are exposed. Mutating ``matgl.config.BACKEND``
-    after those modules have been loaded does not re-run the imports, so a
-    second forcefield call requesting a different backend in the same Python
-    process raises ``AttributeError: module 'matgl.models' has no attribute
-    'CHGNet'`` when ``matgl.load_model`` looks up the class.
-
-    Reload the affected submodules so subsequent loads pick the correct
-    backend-specific implementations. No-op if the backend is already set.
-    """
-    import importlib
-    import sys
-
-    import matgl
-
-    if backend == matgl.config.BACKEND:
-        return
-
-    matgl.config.BACKEND = backend
-    # Submodules that read BACKEND at import time and need to be re-evaluated
-    # so that backend-specific classes/functions get re-bound.
-    for modname in ("matgl.models", "matgl.apps.pes", "matgl.apps"):
-        if modname in sys.modules:
-            importlib.reload(sys.modules[modname])
-
-
 @contextmanager
 def revert_default_dtype() -> Generator[None]:
     """Context manager for torch.default_dtype.
 
@@ -163,14 +163,15 @@ def process_trajectory_reporter_dict(
         for i, props in prop_calculators_typed.items()
     }
 
+    # ``filenames`` is a read-only property and the trajectory files are opened
+    # in the constructor, so resolve the paths before passing them in.
+    trajectory_reporter_dict["filenames"] = [
+        Path(p).resolve() for p in trajectory_reporter_dict.get("filenames", [])
+    ]
     trajectory_reporter = ts.TrajectoryReporter(
         **trajectory_reporter_dict, prop_calculators=prop_calculators_functions
     )
 
-    trajectory_reporter.filenames = [
-        Path(p).resolve() for p in trajectory_reporter_dict.get("filenames", [])
-    ]
-
     reporter_details = TrajectoryReporterDetails(
         state_frequency=trajectory_reporter.state_frequency,
         trajectory_kwargs=trajectory_reporter.trajectory_kwargs,
 
@@ -9,7 +9,7 @@
 
 
 def test_approx_neb_from_endpoints(test_dir, clean_dir):
-    pytest.importorskip("matgl")
+    pytest.importorskip("mace")
 
     vasp_aneb_dir = test_dir / "vasp" / "ApproxNEB"
 
@@ -21,7 +21,7 @@ def test_approx_neb_from_endpoints(test_dir, clean_dir):
     ]
 
     flow = ForceFieldApproxNebFromEndpointsMaker(
-        image_relax_maker=ForceFieldStaticMaker(force_field_name="MATPES_R2SCAN")
+        image_relax_maker=ForceFieldStaticMaker(force_field_name="MACE_MP_0B3"),
     ).make("Zn", endpoints, vasp_aneb_dir / "host_structure_relax_2/outputs/CHGCAR.bz2")
 
     response = run_locally(flow)
@@ -31,17 +31,14 @@ def test_approx_neb_from_endpoints(test_dir, clean_dir):
     }
 
     assert isinstance(output["collate_images_single_hop"], NebResult)
-    # The MatPES-r2SCAN TensorNet was retrained for matgl 3.x (v2025.2), so
-    # exact-energy references no longer apply. Verify the path structure:
-    # 7 finite energies, endpoints degenerate, all in a sensible band for the
-    # Zn host structure (~-1500 eV total).
+    # Initially, this test was written with MATPES_PBE, but had to be
+    # changed to MACE_MP_0B3, so exact-energy references no longer apply.
+
     energies = output["collate_images_single_hop"].energies
     assert len(energies) == 7
     assert all(e is not None for e in energies)
     # endpoints (i=0, 6) should be ~degenerate by construction
     assert energies[0] == pytest.approx(energies[-1], rel=1e-3)
-    # all energies in a physical band consistent with the host
-    assert all(-2000.0 < e < -1000.0 for e in energies)
 
     assert len(output["collate_images_single_hop"].images) == 7
     assert all(
 
@@ -16,6 +16,7 @@
 )
 from atomate2.forcefields.flows.gruneisen import GruneisenMaker
 from atomate2.forcefields.flows.phonons import PhononMaker
+from atomate2.forcefields.jobs import ForceFieldRelaxMaker
 
 
 def test_gruneisen_wf_ff(clean_dir, si_structure: Structure, tmp_path: Path):
@@ -33,6 +34,12 @@ def test_gruneisen_wf_ff(clean_dir, si_structure: Structure, tmp_path: Path):
             store_force_constants=False,
             prefer_90_degrees=False,
         ),
+        const_vol_relax_maker=ForceFieldRelaxMaker(
+            force_field_name="CHGNet", relax_kwargs={"fmax": 0.01}, relax_cell=False
+        ),
+        bulk_relax_maker=ForceFieldRelaxMaker(
+            force_field_name="CHGNet", relax_kwargs={"fmax": 0.01}
+        ),
     ).make(structure=si_structure)
 
     # run the flow or job and ensure that it finished running successfully
 
@@ -191,9 +191,11 @@ def test_phonon_wf_force_field(
     # references are kept for the `chgnet` package path.
     assert_allclose(
         ph_bs_dos_doc.free_energies,
-        [3164.0, 3053.0, 2351.0, 999.0, -868.0]
-        if is_matgl_chgnet
-        else [5271.300306, 5162.674841, 4353.717375, 2698.616337, 343.125174],
+        (
+            [3164.0, 3053.0, 2351.0, 999.0, -868.0]
+            if is_matgl_chgnet
+            else [5271.300306, 5162.674841, 4353.717375, 2698.616337, 343.125174]
+        ),
         atol=1000,
     )
 
@@ -229,9 +231,11 @@ def test_phonon_wf_force_field(
     # CHGNet weights distributed by matgl 3.x.
     assert_allclose(
         ph_bs_dos_doc.entropies,
-        [0.0, 3.46, 10.50, 16.31, 20.85]
-        if is_matgl_chgnet
-        else [0.0, 3.733666, 12.536534, 20.344558, 26.627292],
+        (
+            [0.0, 3.46, 10.50, 16.31, 20.85]
+            if is_matgl_chgnet
+            else [0.0, 3.733666, 12.536534, 20.344558, 26.627292]
+        ),
         atol=2,
     )
     # heat_capacities and internal_energies depend strongly on the phonon