build neighbor list with external Python program

deepmd/calculator.py

@@ -45,6 +45,8 @@ class DP(Calculator):
     type_dict : Dict[str, int], optional
         mapping of element types and their numbers, best left None and the calculator
         will infer this information from model, by default None
+    neighbor_list : ase.neighborlist.NeighborList, optional
+        The neighbor list object. If None, then build the native neighbor list.
 
     Examples
     --------
@@ -83,10 +85,11 @@ def __init__(
         model: Union[str, "Path"],
         label: str = "DP",
         type_dict: Optional[Dict[str, int]] = None,
+        neighbor_list=None,
         **kwargs,
     ) -> None:
         Calculator.__init__(self, label=label, **kwargs)
-        self.dp = DeepPotential(str(Path(model).resolve()))
+        self.dp = DeepPotential(str(Path(model).resolve()), neighbor_list=neighbor_list)
         if type_dict:
             self.type_dict = type_dict
         else:

deepmd/infer/__init__.py

@@ -58,6 +58,7 @@ def DeepPotential(
     load_prefix: str = "load",
     default_tf_graph: bool = False,
     input_map: Optional[dict] = None,
+    neighbor_list=None,
 ) -> Union[DeepDipole, DeepGlobalPolar, DeepPolar, DeepPot, DeepDOS, DeepWFC]:
     """Factory function that will inialize appropriate potential read from `model_file`.
 
@@ -71,6 +72,8 @@ def DeepPotential(
         If uses the default tf graph, otherwise build a new tf graph for evaluation
     input_map : dict, optional
         The input map for tf.import_graph_def. Only work with default tf graph
+    neighbor_list : ase.neighborlist.NeighborList, optional
+        The neighbor list object. If None, then build the native neighbor list.
 
     Returns
     -------
@@ -97,6 +100,7 @@ def DeepPotential(
             load_prefix=load_prefix,
             default_tf_graph=default_tf_graph,
             input_map=input_map,
+            neighbor_list=neighbor_list,
         )
     elif model_type == "dos":
         dp = DeepDOS(
@@ -111,20 +115,23 @@ def DeepPotential(
             load_prefix=load_prefix,
             default_tf_graph=default_tf_graph,
             input_map=input_map,
+            neighbor_list=neighbor_list,
         )
     elif model_type == "polar":
         dp = DeepPolar(
             mf,
             load_prefix=load_prefix,
             default_tf_graph=default_tf_graph,
             input_map=input_map,
+            neighbor_list=neighbor_list,
         )
     elif model_type == "global_polar":
         dp = DeepGlobalPolar(
             mf,
             load_prefix=load_prefix,
             default_tf_graph=default_tf_graph,
             input_map=input_map,
+            neighbor_list=neighbor_list,
         )
     elif model_type == "wfc":
         dp = DeepWFC(

deepmd/infer/deep_dipole.py

@@ -27,6 +27,8 @@ class DeepDipole(DeepTensor):
         If uses the default tf graph, otherwise build a new tf graph for evaluation
     input_map : dict, optional
         The input map for tf.import_graph_def. Only work with default tf graph
+    neighbor_list : ase.neighborlist.NeighborList, optional
+        The neighbor list object. If None, then build the native neighbor list.
 
     Warnings
     --------
@@ -41,6 +43,7 @@ def __init__(
         load_prefix: str = "load",
         default_tf_graph: bool = False,
         input_map: Optional[dict] = None,
+        neighbor_list=None,
     ) -> None:
         # use this in favor of dict update to move attribute from class to
         # instance namespace
@@ -58,6 +61,7 @@ def __init__(
             load_prefix=load_prefix,
             default_tf_graph=default_tf_graph,
             input_map=input_map,
+            neighbor_list=neighbor_list,
         )
 
     def get_dim_fparam(self) -> int:

deepmd/infer/deep_eval.py

@@ -45,6 +45,9 @@ class DeepEval:
         as the initial batch size.
     input_map : dict, optional
         The input map for tf.import_graph_def. Only work with default tf graph
+    neighbor_list : ase.neighborlist.NewPrimitiveNeighborList, optional
+        The ASE neighbor list class to produce the neighbor list. If None, the
+        neighbor list will be built natively in the model.
     """
 
     load_prefix: str  # set by subclass
@@ -56,6 +59,7 @@ def __init__(
         default_tf_graph: bool = False,
         auto_batch_size: Union[bool, int, AutoBatchSize] = False,
         input_map: Optional[dict] = None,
+        neighbor_list=None,
     ):
         self.graph = self._load_graph(
             model_file,
@@ -86,6 +90,8 @@ def __init__(
         else:
             raise TypeError("auto_batch_size should be bool, int, or AutoBatchSize")
 
+        self.neighbor_list = neighbor_list
+
     @property
     @lru_cache(maxsize=None)
     def model_type(self) -> str:
@@ -360,3 +366,92 @@ def eval_typeebd(self) -> np.ndarray:
         t_typeebd = self._get_tensor("t_typeebd:0")
         [typeebd] = run_sess(self.sess, [t_typeebd], feed_dict={})
         return typeebd
+
+    def build_neighbor_list(
+        self,
+        coords: np.ndarray,
+        cell: Optional[np.ndarray],
+        atype: np.ndarray,
+        imap: np.ndarray,
+        neighbor_list,
+    ):
+        """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]
+        atype : np.ndarray
+            The type of atoms. Should be of shape [natoms]
+        imap : np.ndarray
+            The index map of atoms. Should be of shape [natoms]
+        neighbor_list : ase.neighborlist.NewPrimitiveNeighborList
+            ASE neighbor list. The following method or attribute will be
+            used/set: bothways, self_interaction, update, build, first_neigh,
+            pair_second, offset_vec.
+
+        Returns
+        -------
+        natoms_vec : np.ndarray
+            The number of atoms. This tensor has the length of Ntypes + 2
+            natoms[0]: nloc
+            natoms[1]: nall
+            natoms[i]: 2 <= i < Ntypes+2, number of type i atoms for nloc
+        coords : np.ndarray
+            The coordinates of atoms, including ghost atoms. Should be of
+            shape [nframes, nall, 3]
+        atype : np.ndarray
+            The type of atoms, including ghost atoms. Should be of shape [nall]
+        mesh : np.ndarray
+            The mesh in nei_mode=4.
+        imap : np.ndarray
+            The index map of atoms. Should be of shape [nall]
+        ghost_map : np.ndarray
+            The index map of ghost atoms. Should be of shape [nghost]
+        """
+        pbc = np.repeat(cell is not None, 3)
+        cell = cell.reshape(3, 3)
+        positions = coords.reshape(-1, 3)
+        neighbor_list.bothways = True
+        neighbor_list.self_interaction = False
+        if neighbor_list.update(pbc, cell, positions):
+            neighbor_list.build(pbc, cell, positions)
+        first_neigh = neighbor_list.first_neigh.copy()
+        pair_second = neighbor_list.pair_second.copy()
+        offset_vec = neighbor_list.offset_vec.copy()
+        # get out-of-box neighbors
+        out_mask = np.any(offset_vec != 0, axis=1)
+        out_idx = pair_second[out_mask]
+        out_offset = offset_vec[out_mask]
+        out_coords = positions[out_idx] + out_offset.dot(cell)
+        atype = np.array(atype, dtype=int)
+        out_atype = atype[out_idx]
+
+        nloc = positions.shape[0]
+        nghost = out_idx.size
+        all_coords = np.concatenate((positions, out_coords), axis=0)
+        all_atype = np.concatenate((atype, out_atype), axis=0)
+        # convert neighbor indexes
+        ghost_map = pair_second[out_mask]
+        pair_second[out_mask] = np.arange(nloc, nloc + nghost)
+        # get the mesh
+        mesh = np.zeros(16 + nloc * 2 + pair_second.size, dtype=int)
+        mesh[0] = nloc
+        # ilist
+        mesh[16 : 16 + nloc] = np.arange(nloc)
+        # numnei
+        mesh[16 + nloc : 16 + nloc * 2] = first_neigh[1:] - first_neigh[:-1]
+        # jlist
+        mesh[16 + nloc * 2 :] = pair_second
+
+        # natoms_vec
+        natoms_vec = np.zeros(self.ntypes + 2).astype(int)
+        natoms_vec[0] = nloc
+        natoms_vec[1] = nloc + nghost
+        for ii in range(self.ntypes):
+            natoms_vec[ii + 2] = np.count_nonzero(atype == ii)
+        # imap append ghost atoms
+        imap = np.concatenate((imap, np.arange(nloc, nloc + nghost)))
+        return natoms_vec, all_coords, all_atype, mesh, imap, ghost_map

deepmd/infer/deep_polar.py

@@ -30,6 +30,8 @@ class DeepPolar(DeepTensor):
         If uses the default tf graph, otherwise build a new tf graph for evaluation
     input_map : dict, optional
         The input map for tf.import_graph_def. Only work with default tf graph
+    neighbor_list : ase.neighborlist.NeighborList, optional
+        The neighbor list object. If None, then build the native neighbor list.
 
     Warnings
     --------
@@ -44,6 +46,7 @@ def __init__(
         load_prefix: str = "load",
         default_tf_graph: bool = False,
         input_map: Optional[dict] = None,
+        neighbor_list=None,
     ) -> None:
         # use this in favor of dict update to move attribute from class to
         # instance namespace
@@ -61,6 +64,7 @@ def __init__(
             load_prefix=load_prefix,
             default_tf_graph=default_tf_graph,
             input_map=input_map,
+            neighbor_list=neighbor_list,
         )
 
     def get_dim_fparam(self) -> int:
@@ -83,10 +87,16 @@ class DeepGlobalPolar(DeepTensor):
         The prefix in the load computational graph
     default_tf_graph : bool
         If uses the default tf graph, otherwise build a new tf graph for evaluation
+    neighbor_list : ase.neighborlist.NeighborList, optional
+        The neighbor list object. If None, then build the native neighbor list.
     """
 
     def __init__(
-        self, model_file: str, load_prefix: str = "load", default_tf_graph: bool = False
+        self,
+        model_file: str,
+        load_prefix: str = "load",
+        default_tf_graph: bool = False,
+        neighbor_list=None,
     ) -> None:
         self.tensors.update(
             {
@@ -101,6 +111,7 @@ def __init__(
             model_file,
             load_prefix=load_prefix,
             default_tf_graph=default_tf_graph,
+            neighbor_list=None,
         )
 
     def eval(