core.models.gemnet_oc.utils

core.models.gemnet_oc.utils#

Copyright (c) Meta, Inc. and its affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Functions#

ragged_range(sizes)

Multiple concatenated ranges.

repeat_blocks(→ torch.Tensor)

Repeat blocks of indices.

masked_select_sparsetensor_flat(...)

calculate_interatomic_vectors(R, id_s, id_t, offsets_st)

Calculate the vectors connecting the given atom pairs,

inner_product_clamped(→ torch.Tensor)

Calculate the inner product between the given normalized vectors,

get_angle(→ torch.Tensor)

Calculate angles between atoms c -> a <- b.

vector_rejection(R_ab, P_n)

Project the vector R_ab onto a plane with normal vector P_n.

get_projected_angle(→ torch.Tensor)

Project the vector R_ab onto a plane with normal vector P_n,

mask_neighbors(neighbors, edge_mask)

get_neighbor_order(→ torch.Tensor)

Give a mask that filters out edges so that each atom has at most

get_inner_idx(idx, dim_size)

Assign an inner index to each element (neighbor) with the same index.

get_edge_id(edge_idx, cell_offsets, num_atoms)

Module Contents#

core.models.gemnet_oc.utils.ragged_range(sizes)#

Multiple concatenated ranges.

Examples

sizes = [1 4 2 3] Return: [0 0 1 2 3 0 1 0 1 2]

core.models.gemnet_oc.utils.repeat_blocks(sizes, repeats, continuous_indexing: bool = True, start_idx: int = 0, block_inc: int = 0, repeat_inc: int = 0) → torch.Tensor#

Repeat blocks of indices. Adapted from https://stackoverflow.com/questions/51154989/numpy-vectorized-function-to-repeat-blocks-of-consecutive-elements

continuous_indexing: Whether to keep increasing the index after each block start_idx: Starting index block_inc: Number to increment by after each block,

either global or per block. Shape: len(sizes) - 1

repeat_inc: Number to increment by after each repetition,

either global or per block

Examples

sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = False Return: [0 0 0 0 1 2 0 1 2 0 1 0 1 0 1] sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True Return: [0 0 0 1 2 3 1 2 3 4 5 4 5 4 5] sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; repeat_inc = 4 Return: [0 4 8 1 2 3 5 6 7 4 5 8 9 12 13] sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; start_idx = 5 Return: [5 5 5 6 7 8 6 7 8 9 10 9 10 9 10] sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; block_inc = 1 Return: [0 0 0 2 3 4 2 3 4 6 7 6 7 6 7] sizes = [0,3,2] ; repeats = [3,2,3] ; continuous_indexing = True Return: [0 1 2 0 1 2 3 4 3 4 3 4] sizes = [2,3,2] ; repeats = [2,0,2] ; continuous_indexing = True Return: [0 1 0 1 5 6 5 6]

core.models.gemnet_oc.utils.masked_select_sparsetensor_flat(src, mask) → torch_sparse.SparseTensor#
core.models.gemnet_oc.utils.calculate_interatomic_vectors(R, id_s, id_t, offsets_st)#

Calculate the vectors connecting the given atom pairs, considering offsets from periodic boundary conditions (PBC).

Parameters:

  • R (Tensor, shape = (nAtoms, 3)) – Atom positions.

  • id_s (Tensor, shape = (nEdges,)) – Indices of the source atom of the edges.

  • id_t (Tensor, shape = (nEdges,)) – Indices of the target atom of the edges.

  • offsets_st (Tensor, shape = (nEdges,)) – PBC offsets of the edges. Subtract this from the correct direction.

Returns:

(D_st, V_st) –

D_st: Tensor, shape = (nEdges,)

Distance from atom t to s.

V_st: Tensor, shape = (nEdges,)

Unit direction from atom t to s.

Return type:

tuple

core.models.gemnet_oc.utils.inner_product_clamped(x, y) → torch.Tensor#

Calculate the inner product between the given normalized vectors, giving a result between -1 and 1.

core.models.gemnet_oc.utils.get_angle(R_ac, R_ab) → torch.Tensor#

Calculate angles between atoms c -> a <- b.

Parameters:

  • R_ac (Tensor, shape = (N, 3)) – Vector from atom a to c.

  • R_ab (Tensor, shape = (N, 3)) – Vector from atom a to b.

Returns:

angle_cab – Angle between atoms c <- a -> b.

Return type:

Tensor, shape = (N,)

core.models.gemnet_oc.utils.vector_rejection(R_ab, P_n)#

Project the vector R_ab onto a plane with normal vector P_n.

Parameters:

  • R_ab (Tensor, shape = (N, 3)) – Vector from atom a to b.

  • P_n (Tensor, shape = (N, 3)) – Normal vector of a plane onto which to project R_ab.

Returns:

R_ab_proj – Projected vector (orthogonal to P_n).

Return type:

Tensor, shape = (N, 3)

core.models.gemnet_oc.utils.get_projected_angle(R_ab, P_n, eps: float = 0.0001) → torch.Tensor#

Project the vector R_ab onto a plane with normal vector P_n, then calculate the angle w.r.t. the (x [cross] P_n), or (y [cross] P_n) if the former would be ill-defined/numerically unstable.

Parameters:

  • R_ab (Tensor, shape = (N, 3)) – Vector from atom a to b.

  • P_n (Tensor, shape = (N, 3)) – Normal vector of a plane onto which to project R_ab.

  • eps (float) – Norm of projection below which to use the y-axis instead of x.

Returns:

angle_ab – Angle on plane w.r.t. x- or y-axis.

Return type:

Tensor, shape = (N)

core.models.gemnet_oc.utils.mask_neighbors(neighbors, edge_mask)#
core.models.gemnet_oc.utils.get_neighbor_order(num_atoms: int, index, atom_distance) → torch.Tensor#

Give a mask that filters out edges so that each atom has at most max_num_neighbors_threshold neighbors.

core.models.gemnet_oc.utils.get_inner_idx(idx, dim_size)#

Assign an inner index to each element (neighbor) with the same index. For example, with idx=[0 0 0 1 1 1 1 2 2] this returns [0 1 2 0 1 2 3 0 1]. These indices allow reshape neighbor indices into a dense matrix. idx has to be sorted for this to work.

core.models.gemnet_oc.utils.get_edge_id(edge_idx, cell_offsets, num_atoms: int)#
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