deepmd.pt.utils.nlist
Module Contents
Functions
| |
| Build neightbor list for a single frame. keeps nsel neighbors. |
| Given a nlist that does not distinguish atom types, return a nlist that |
| |
| Input one neighbor list, and produce multiple neighbor lists with |
| Extend the coordinates of the atoms by appending peridoc images. |
- deepmd.pt.utils.nlist.extend_input_and_build_neighbor_list(coord, atype, rcut: float, sel: List[int], mixed_types: bool = False, box: torch.Tensor | None = None)[source]
- deepmd.pt.utils.nlist.build_neighbor_list(coord: torch.Tensor, atype: torch.Tensor, nloc: int, rcut: float, sel: int | List[int], distinguish_types: bool = True) torch.Tensor [source]
Build neightbor list for a single frame. keeps nsel neighbors.
- Parameters:
- coord
torch.Tensor
exptended coordinates of shape [batch_size, nall x 3]
- atype
torch.Tensor
extended atomic types of shape [batch_size, nall] if type < 0 the atom is treat as virtual atoms.
- nloc
int
number of local atoms.
- rcut
float
cut-off radius
- sel
int
orList
[int
] maximal number of neighbors (of each type). if distinguish_types==True, nsel should be list and the length of nsel should be equal to number of types.
- distinguish_typesbool
distinguish different types.
- coord
- Returns:
- neighbor_list
torch.Tensor
Neighbor list of shape [batch_size, nloc, nsel], the neighbors are stored in an ascending order. If the number of neighbors is less than nsel, the positions are masked with -1. The neighbor list of an atom looks like |------ nsel ------| xx xx xx xx -1 -1 -1 if distinguish_types==True and we have two types |---- nsel[0] -----| |---- nsel[1] -----| xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1 For virtual atoms all neighboring positions are filled with -1.
- neighbor_list
- deepmd.pt.utils.nlist.nlist_distinguish_types(nlist: torch.Tensor, atype: torch.Tensor, sel: List[int])[source]
Given a nlist that does not distinguish atom types, return a nlist that distinguish atom types.
- deepmd.pt.utils.nlist.build_multiple_neighbor_list(coord: torch.Tensor, nlist: torch.Tensor, rcuts: List[float], nsels: List[int]) Dict[str, torch.Tensor] [source]
Input one neighbor list, and produce multiple neighbor lists with different cutoff radius and numbers of selection out of it. The required rcuts and nsels should be smaller or equal to the input nlist.
- Parameters:
- coord
torch.Tensor
exptended coordinates of shape [batch_size, nall x 3]
- nlist
torch.Tensor
Neighbor list of shape [batch_size, nloc, nsel], the neighbors should be stored in an ascending order.
- rcuts
List
[float
] list of cut-off radius in ascending order.
- nsels
List
[int
] maximal number of neighbors in ascending order.
- coord
- Returns:
- deepmd.pt.utils.nlist.extend_coord_with_ghosts(coord: torch.Tensor, atype: torch.Tensor, cell: torch.Tensor | None, rcut: float, cell_cpu: torch.Tensor | None = None)[source]
Extend the coordinates of the atoms by appending peridoc images. The number of images is large enough to ensure all the neighbors within rcut are appended.
- Parameters:
- coord
torch.Tensor
original coordinates of shape [-1, nloc*3].
- atype
torch.Tensor
atom type of shape [-1, nloc].
- cell
torch.Tensor
simulation cell tensor of shape [-1, 9].
- rcut
float
the cutoff radius
- cell_cpu
torch.Tensor
cell on cpu for performance
- coord
- Returns:
- extended_coord:
torch.Tensor
extended coordinates of shape [-1, nall*3].
- extended_atype:
torch.Tensor
extended atom type of shape [-1, nall].
- index_mapping:
torch.Tensor
maping extended index to the local index
- extended_coord: