Bayesian optimization and prediction of the durability of triple-halide perovskite thin films under light and heat stressors

Abstract

Perovskite thin films are leading candidates for the wide bandgap active layer in tandem solar cells; however, they are plagued by instability at elevated temperature, and under illumination. The large compositional design space of perovskites offers hope to engineer stability but is difficult to search effectively due to the generally low reproducibility in processing perovskite thin films. Here, we employ Bayesian optimization (BO) in conjunction with precision automation of perovskite thin film processing to improve the stability of perovskite films with a bandgap of ≈1.65 eV. The BO framework results in a 2.5× increase in the learning rate compared to traditional grid search. Additionally, we present a regression model that provides the first robust prediction of perovskite stability under light and heat based on readily-measured photoluminescence properties. This regression model achieves reasonable predictive power (coefficient of variation of the root mean square error = 27%) for the task of correlating fast and simple optical metrology with resource-intensive standardized light and heat tests that run for hundreds of hours. Feature importance ranking confirms Br content and photoluminescence emission stability under intense light as key indicators of durability.