mlcolvar.core.transform.descriptors.PairwiseDistances

class mlcolvar.core.transform.descriptors.PairwiseDistances(n_atoms: int, PBC: bool, cell: float | list, scaled_coords: bool = False, slicing_pairs: list = None)

Bases: Transform

Non duplicated pairwise distances for a set of atoms from their positions. Can compute either all the distances or a subset.

__init__(n_atoms: int, PBC: bool, cell: float | list, scaled_coords: bool = False, slicing_pairs: list = None) → Tensor

Initialize a pairwise distances object. Can compute either all the distances or a subset based on the slicing_pairs key.

Parameters:

• n_atoms (int) – Number of atoms in the system

• PBC (bool) – Switch for Periodic Boundary Conditions use

• cell (Union[float, list]) – Dimensions of the real cell, orthorombic-like cells only

• scaled_coords (bool) – Switch for coordinates scaled on cell’s vectors use, by default False

• slicing_pairs (list) – indices of the subset of distances to be returned, by default None

Returns:

Non duplicated pairwise distances

Return type:

torch.Tensor

Methods

__init__(n_atoms, PBC, cell[, ...])	Initialize a pairwise distances object.
compute_pairwise_distances(pos)
forward(x)	Define the computation performed at every call.

forward(x: Tensor)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

setup_from_datamodule(datamodule)

Initialize parameters based on pytorch lighting datamodule.

Attributes

T_destination
call_super_init
dump_patches
training