Machine Learning Enables Prediction of Halide Perovskites' Optical Behavior with >90% Accuracy.

The composition-dependent degradation of hybrid organic-inorganic perovskites (HOIPs) due to environmental stressors still precludes their commercialization. It is very difficult to quantify their behavior upon exposure to each stressor by exclusively using trial-and-error methods due to the high-dimensional parameter space involved. We implement machine learning (ML) models using high-throughput, photoluminescence (PL) to predict the response of Cs FA Pb(Br I ) while exposed to relative humidity cycles.