Screening of novel halide perovskites for photocatalytic water splitting using multi-fidelity machine learning

Abstract

> 20%), building upon our recently published computational data and machine learning (ML) models. Our multi-fidelity density functional theory (DFT) dataset comprises decomposition energies and band gaps of nearly 1000 pure and alloyed perovskite halides using both the GGA-PBE and HSE06 functionals. Using rigorously optimized composition-based ML regression models, we performed screening across a chemical space of 150 000+ halide perovskites to yield hundreds of stable compounds with suitable band gaps and edges for photocatalytic water splitting. A handful of the best candidates were investigated with in-depth DFT computations to validate their properties. This work presents a framework for accelerating the navigation of a massive chemical space of halide perovskite alloys and understanding their potential utility for water splitting and motivates future efforts towards the synthesis and characterization of the most promising materials.