Sparse representation for machine learning the properties of defects in 2D materials

Sparse representation of crystals with defects

For machine learning algorithms, an atomic structure is a so-called point cloud: a set of points in 3D space. Each point is associated with a vector of properties, which at the least contains the atomic number, but may also include more physics-based features, such as radius, the number of valence electrons, etc.

The structures with defects present a challenge to machine learning algorithms. The neighborhoods of the majority of the atoms are not affected by the point defects. In principle, this shouldn’t be an obstacle for a perfect algorithm….