structuretoolkit.analyse.spatial.get_interstitials

structuretoolkit.analyse.spatial.get_interstitials(structure: Atoms, num_neighbors: int, n_gridpoints_per_angstrom: int = 5, min_distance: float = 1, use_voronoi: bool = False, x_eps: float = 0.1, l_values: ndarray = array([2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), q_eps: float = 0.3, var_ratio: float = 5.0, min_samples: int | None = None, neigh_args: dict | None = None, **kwargs) → Interstitials

Function to search for interstitial sites

This class internally does the following steps:

0. Initialize grid points (or Voronoi vertices) which are considered as

   interstitial site candidates.

1. Eliminate points within a distance from the nearest neighboring atoms as

   given by min_distance

2. Shift interstitial candidates to the nearest symmetric points with respect to the

   neighboring atom sites/vertices.

3. Cluster interstitial candidate positions to avoid point overlapping.

4. Cluster interstitial candidates by their Steinhardt parameters (cf. l_values for

   the values of the spherical harmonics) and the variance of the distances and take the group with the smallest average distance variance

The interstitial sites can be obtained via positions

In complex structures (i.e. grain boundary, dislocation etc.), the default parameters should be chosen properly. In order to see other quantities, which potentially characterize interstitial sites, see the following class methods:

• get_variances()

• get_distances()

• get_steinhardt_parameters()

• get_volumes()

• get_areas()

Troubleshooting:

Identifying interstitial sites is not a very easy task. The algorithm employed here will probably do a good job, but if it fails, it might be good to look at the following points

• The intermediate results can be accessed via run_workflow by specifying the step number.

• The most vulnerable point is the last step, clustering by Steinhardt parameters. Take a

  look at the step before and after. If the interstitial sites are present in the step before the clustering by Steinhardt parameters, you might want to change the values of q_eps and var_ratio. It might make a difference to use l_values as well.

• It might fail to find sites if the box is very small. In that case it might make sense to

  set min_samples very low (you can take 1)

Parameters:

• num_neighbors (int) – Number of neighbors/vertices to consider for the interstitial sites. By definition, tetrahedral sites should have 4 vertices and octahedral sites 6.

• n_gridpoints_per_angstrom (int) – Number of grid points per angstrom for the initialization of the interstitial candidates. The finer the mesh (i.e. the larger the value), the likelier it is to find the correct sites but then also it becomes computationally more expensive. Ignored if use_voronoi is set to True

• min_distance (float) – Minimum distance from the nearest neighboring atoms to the positions for them to be considered as interstitial site candidates. Set min_distance to 0 if no point should be removed.

• use_voronoi (bool) – Use Voronoi vertices for the initial interstitial candidate positions instead of grid points.

• x_eps (bool) – eps value for the clustering of interstitial candidate positions

• l_values (list) – list of values for the Steinhardt parameter values for the classification of the interstitial candidate points

• q_eps (float) – eps value for the clustering of interstitial candidate points based on Steinhardt parameters and distance variances. This might play a crucial role in identifying the correct interstitial sites

• var_ratio (float) – factor to be multiplied to the variance values in order to give a larger weight to the variances.

• min_samples (int/None) – min_sample in the point clustering.

• neigh_args (dict) – arguments to be added to get_neighbors