Machine learning to derive quantum-informed and chemically-aware force fields to simulate interfaces and defects in hybrid halide perovskites

The paradigm for creating materials for energy applications is no longer simply discovering a single material but instead involves combining multiple materials to achieve a desired functionality. Simulating interfaces between disparate materials or entire devices requires large systems, often approximated by classical simulations based on force fields (FFs). The accuracy of such simulations can be questionable because standard FFs may not accurately respond to changing chemical environments near an interface. We demonstrate a machine learning (ML) approach to predict quantum-derived atomic properties (e.g., charge, dipole moment, etc.) from descriptors of the local environment. The properties are used to compute chemically-aware, many-body inter-atomic forces, because the local environment descriptors encode more than just pairwise information. We apply these ML-derived FFs to several all-inorganic halide perovskite systems, CsBX₃ (B = Sn, Pb, X = Br, I) with local and extended defects, and to the interfaces between these materials, to correctly capture the anomalous charge and electrostatic multipole dynamics observed in these systems. We report on the MLFFs methods developed, and the resulting materials implications of charge dynamics on halide perovskite functionality.