fix(tf): preserve non-PBC ASE neighbor semantics

Keep the original open-boundary decision separate from the identity box required by TensorFlow feeds, and let both ASE builders handle a missing cell without creating periodic ghosts.

Add collected DeepPotential and DeepTensor regressions for non-periodic external neighbor-list inference.

Coding-Agent: Codex
Codex-Version: codex-cli 0.144.4
Model: gpt-5.6-sol
Reasoning-Effort: xhigh

Author: njzjz-bot

deepmd/tf/infer/deep_eval.py

@@ -549,15 +549,23 @@ def build_neighbor_list(
         atype: np.ndarray,
         imap: np.ndarray,
         neighbor_list: "ase.neighborlist.NeighborList | None",
-    ) -> tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+    ]:
         """Make the mesh with neighbor list for a single frame.
 
         Parameters
         ----------
         coords : np.ndarray
             The coordinates of atoms. Should be of shape [natoms, 3]
         cell : Optional[np.ndarray]
-            The cell of the system. Should be of shape [3, 3]
+            The cell of the system. Should be of shape [3, 3]. None denotes
+            open boundary conditions.
         atype : np.ndarray
             The type of atoms. Should be of shape [natoms]
         imap : np.ndarray
@@ -587,7 +595,11 @@ def build_neighbor_list(
             The index map of ghost atoms. Should be of shape [nghost]
         """
         pbc = np.repeat(cell is not None, 3)
-        cell = cell.reshape(3, 3)
+        # ASE still requires a 3x3 cell for non-periodic systems, but the cell
+        # must not be used to infer periodicity or create ghost atoms.
+        cell = (
+            np.zeros((3, 3), dtype=coords.dtype) if cell is None else cell.reshape(3, 3)
+        )
         positions = coords.reshape(-1, 3)
         neighbor_list.bothways = True
         neighbor_list.self_interaction = False
@@ -814,6 +826,9 @@ def _prepare_feed_dict(
         else:
             pbc = True
             cells = np.array(cells).reshape([nframes, 9])
+        # Keep the original boundary semantics separate from the identity box
+        # used only to satisfy TensorFlow's non-optional box placeholder.
+        neighbor_cell = cells if pbc else None
 
         if self.has_fparam:
             assert fparam is not None
@@ -884,7 +899,7 @@ def _prepare_feed_dict(
                 ghost_map,
             ) = self.build_neighbor_list(
                 coords,
-                cells if cells is not None else None,
+                neighbor_cell,
                 atom_types,
                 imap,
                 self.neighbor_list,
@@ -1534,15 +1549,23 @@ def build_neighbor_list(
         atype: np.ndarray,
         imap: np.ndarray,
         neighbor_list: "ase.neighborlist.NeighborList | None",
-    ) -> tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+        np.ndarray,
+    ]:
         """Make the mesh with neighbor list for a single frame.
 
         Parameters
         ----------
         coords : np.ndarray
             The coordinates of atoms. Should be of shape [natoms, 3]
         cell : Optional[np.ndarray]
-            The cell of the system. Should be of shape [3, 3]
+            The cell of the system. Should be of shape [3, 3]. None denotes
+            open boundary conditions.
         atype : np.ndarray
             The type of atoms. Should be of shape [natoms]
         imap : np.ndarray
@@ -1572,7 +1595,11 @@ def build_neighbor_list(
             The index map of ghost atoms. Should be of shape [nghost]
         """
         pbc = np.repeat(cell is not None, 3)
-        cell = cell.reshape(3, 3)
+        # ASE still requires a 3x3 cell for non-periodic systems, but the cell
+        # must not be used to infer periodicity or create ghost atoms.
+        cell = (
+            np.zeros((3, 3), dtype=coords.dtype) if cell is None else cell.reshape(3, 3)
+        )
         positions = coords.reshape(-1, 3)
         neighbor_list.bothways = True
         neighbor_list.self_interaction = False

deepmd/tf/infer/deep_tensor.py

@@ -202,6 +202,9 @@ def eval(
         else:
             pbc = True
             cells = np.array(cells).reshape([nframes, 9])
+        # Keep the original boundary semantics separate from the identity box
+        # used only to satisfy TensorFlow's non-optional box placeholder.
+        neighbor_cell = cells if pbc else None
 
         # sort inputs
         coords, atom_types, imap, sel_at, sel_imap = self.sort_input(
@@ -227,7 +230,7 @@ def eval(
                 _,
             ) = self.build_neighbor_list(
                 coords,
-                cells if cells is not None else None,
+                neighbor_cell,
                 atom_types,
                 imap,
                 self.neighbor_list,
@@ -346,6 +349,9 @@ def eval_full(
         else:
             pbc = True
             cells = np.array(cells).reshape([nframes, 9])
+        # Keep the original boundary semantics separate from the identity box
+        # used only to satisfy TensorFlow's non-optional box placeholder.
+        neighbor_cell = cells if pbc else None
         nout = self.output_dim
 
         # sort inputs
@@ -373,7 +379,7 @@ def eval_full(
                 ghost_map,
             ) = self.build_neighbor_list(
                 coords,
-                cells if cells is not None else None,
+                neighbor_cell,
                 atom_types,
                 imap,
                 self.neighbor_list,

source/tests/infer/test_models.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import unittest
 
-import ase
+import ase.neighborlist
 import dpdata
 import numpy as np
 
@@ -421,3 +421,36 @@ def test_2frame_atm(self) -> None:
     @unittest.skip("Zero atoms not supported")
     def test_zero_input(self) -> None:
         pass
+
+
+def test_deep_pot_neighbor_list_nopbc() -> None:
+    """The ASE path must preserve a testcase's open-boundary semantics."""
+    # This is intentionally standalone: the parameterized TestDeepPot symbol is
+    # replaced by ``object``, so its neighbor-list subclass inherits no tests.
+    case = get_cases()["se_e2_a"]
+    result = next(result for result in case.results if result.box is None)
+    with DeepEval(
+        case.get_model(".pb"),
+        neighbor_list=ase.neighborlist.NewPrimitiveNeighborList(
+            cutoffs=case.rcut,
+            bothways=True,
+        ),
+    ) as dp:
+        ee, ff, vv, ae, av = dp.eval(
+            result.coord,
+            None,
+            result.atype,
+            atomic=True,
+            fparam=result.fparam,
+            aparam=result.aparam,
+        )[:5]
+
+    np.testing.assert_almost_equal(ff.ravel(), result.force.ravel(), STRICT_PLACES)
+    np.testing.assert_almost_equal(
+        ae.ravel(), result.atomic_energy.ravel(), STRICT_PLACES
+    )
+    np.testing.assert_almost_equal(
+        av.ravel(), result.atomic_virial.ravel(), STRICT_PLACES
+    )
+    np.testing.assert_almost_equal(ee.ravel(), result.energy, STRICT_PLACES)
+    np.testing.assert_almost_equal(vv.ravel(), result.virial, STRICT_PLACES)

source/tests/tf/test_deepdipole.py

@@ -1162,3 +1162,32 @@ def test_2frame_full_atm(self) -> None:
     @unittest.skip("multiple frames not supported")
     def test_2frame_old_atm(self) -> None:
         pass
+
+    def test_nopbc_matches_native_neighbor_building(self) -> None:
+        """ASE and native tensor inference must agree for an open system."""
+        native = DeepDipole("deepdipole_new.pb")
+        try:
+            actual_tensor = self.dp.eval(self.coords, None, self.atype, atomic=True)
+            expected_tensor = native.eval(self.coords, None, self.atype, atomic=True)
+            np.testing.assert_almost_equal(
+                actual_tensor,
+                expected_tensor,
+                default_places,
+            )
+
+            actual_full = self.dp.eval_full(
+                self.coords,
+                None,
+                self.atype,
+                atomic=True,
+            )
+            expected_full = native.eval_full(
+                self.coords,
+                None,
+                self.atype,
+                atomic=True,
+            )
+            for actual, expected in zip(actual_full, expected_full, strict=True):
+                np.testing.assert_almost_equal(actual, expected, default_places)
+        finally:
+            native.close()