mlcolvar.core.transform.descriptors.PairwiseDistances

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

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 | None = None, scaled_coords: bool = False, slicing_pairs: list = None) → Tensor[source]

Initialize a pairwise distances object. Can compute either all the distances or a subset based on the slicing_pairs key. The cell size to be used for PBC and/or scaled coordinates needs to be provided. This can be done in one of two ways, exclusively: - Fixed cell (e.g., NVT simulations), at initialization, using the cell keyword, for a fixed cell only.

This mode supports torchscript of the preprocessing module and can be used with the PLUMED interface.

  • Varying cells, at runtime (e.g., NPT simulations), using the cell entry in the forward method or adding the cell data in the dataset used for training. This mode doesn’t support torchscript of the preprocessing module, as it is not supported in the PLUMED interface.

Parameters:

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

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

  • cell (Union[float, list, None]) – Dimensions of the real cell for fixed cell mode, orthorombic-like cells only. For varying cell mode, this argument must be left as None and the cell must be provided at runtime. Note that only fixed cell mode supports torchscript of the preprocessing module.

  • 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. Can compute either all the distances or a subset based on the slicing_pairs key. The cell size to be used for PBC and/or scaled coordinates needs to be provided. This can be done in one of two ways, exclusively: - Fixed cell (e.g., NVT simulations), at initialization, using the cell keyword, for a fixed cell only. This mode supports torchscript of the preprocessing module and can be used with the PLUMED interface. - Varying cells, at runtime (e.g., NPT simulations), using the cell entry in the forward method or adding the cell data in the dataset used for training. This mode doesn't support torchscript of the preprocessing module, as it is not supported in the PLUMED interface.

compute_pairwise_distances(pos[, cell])

forward(x[, cell])

Define the computation performed at every call.
forward(x: Tensor, cell: float | list | Tensor = None)[source]

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