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Metal halide perovskite (MHP) optoelectronics may become a viable alternativeto standard Si-based technologies, but the current lack of long-term stabilityprecludes their commercial adoption. Exposure to standard operational stressors(light, temperature, bias, oxygen, and water) often instigate optical andelectronic dynamics, calling for a systematic investigation into MHPphotophysical processes and the development of quantitative models for theirprediction. We resolve the moisture-driven light emission dynamics for bothmethylammonium lead tribromide and triiodide thin films as a function ofrelative humidity (rH). With the humidity and photoluminescence time series, wetrain recurrent neural networks and establish their ability to quantitativelypredict the path of future light emission with <11% error over 12 hours.Together, our in situ rH-PL measurements and machine learning forecastingmodels provide a framework for the rational design of future stable perovskitedevices and, thus, a faster transition towards commercial applications.

Quantitative predictions of photo-emission dynamics in metal halide perovskites via machine learning