2020/10/08 by John M. Howard, Howard, John M., Qiong Wang +13
Computer Science · Engineering · Materials Science · #Applied Physics (physics.app-ph) #FOS: Physical sciences #Machine Learning and ELM #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Perovskite Materials and Applications
paper · pdf · doi:10.48550/arxiv.2010.03702
openalex publication_date 2020/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Metal halide perovskite (MHP) optoelectronics 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 <11% error over 12 hours. Together, our in situ 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 towards commercial applications.