standard_nl

torch_sim.neighbors.standard.standard_nl(positions, cell, pbc, cutoff, system_idx, self_interaction=False)[source]

Compute neighbor lists using primitive neighbor list algorithm.

Parameters:

  • positions (Tensor) – Atomic positions tensor [n_atoms, 3]

  • cell (Tensor) – Unit cell vectors [n_systems, 3, 3] or [3, 3]

  • pbc (Tensor) – Boolean tensor [n_systems, 3] or [3]

  • cutoff (Tensor) – Maximum distance for considering atoms as neighbors

  • system_idx (Tensor) – Tensor [n_atoms] indicating which system each atom belongs to

  • self_interaction (bool) – If True, include self-pairs. Default: False

Returns:

  • mapping: Tensor [2, num_neighbors] - pairs of atom indices

  • system_mapping: Tensor [num_neighbors] - system assignment for each pair

  • shifts_idx: Tensor [num_neighbors, 3] - periodic shift indices

Return type:

tuple containing

Example

>>> # Single system (all atoms belong to system 0)
>>> positions = torch.tensor([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0]])
>>> cell = torch.eye(3) * 10.0
>>> pbc = torch.tensor([True, True, True])
>>> cutoff = torch.tensor(1.5)
>>> system_idx = torch.zeros(2, dtype=torch.long)
>>> mapping, sys_map, shifts = standard_nl(
...     positions, cell, pbc, cutoff, system_idx
... )

>>> # Batched systems
>>> positions = torch.randn(20, 3)  # 20 atoms total
>>> cell = torch.eye(3).repeat(2, 1) * 10.0  # 2 systems
>>> system_idx = torch.cat([torch.zeros(10), torch.ones(10)]).long()
>>> mapping, sys_map, shifts = standard_nl(
...     positions, cell, pbc, cutoff, system_idx
... )

References