Topological representation of layered hybrid lead halides for machine learning using universal clusters

Prediction of band gaps in layered hybrid halide compounds promising for photovoltaic and optoelectronic applications was performed using a machine learning approach. In order to facilitate the discovery and design of new hybrid halide materials with tailored electronic properties, machine learning models were enhanced with invariant topological representations of these materials using the atom-specific persistent homology method.