Quantitative Predictions of Moisture-Driven Photoemission Dynamics in Metal Halide Perovskites via Machine Learning

Abstract

Metal halide perovskite (MHP) photovoltaics may become a viable alternative to standard Si-based technologies, but the current lack of long-term stability precludes their commercial adoption. Exposure to standard operational stressors (light, temperature, bias, oxygen, and water) often instigate optical and electronic dynamics, calling for a systematic investigation into MHP photophysical processes and the development of quantitative models for their prediction. We resolve the moisture-driven light emission dynamics for both methylammonium lead tribromide and triiodide thin films as a function of relative humidity (rH). With the humidity and photoluminescence time series, we train recurrent neural networks and establish their ability to quantitatively predict the path of future light emission with 18% error over 4 h. Together, our <i>in situ</i> rH-PL measurements and machine learning forecasting models provide a framework for the rational design of future stable perovskite devices and, thus, a faster transition toward commercial applications.