Accelerating the understanding of halide perovskites through machine learning

Machine learning (ML) is a powerful tool to accelerate the development of halide perovskite materials and devices. We apply ML models varying from echo state networks to statistical models to classify and predict physical properties such as hole transport layer electrical conductivity, halide perovskite photoluminescence response, the power conversion efficiency of photovoltaic devices, etc. Specifically, we use in situ environmental optical measurements to predict the optical behavior of Cs-FA perovskites for 50+ hours, upon materials' exposure to moisture.