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

The composition-dependent degradation of hybrid organicinorganic 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, in situ photoluminescence (PL) to predict the response of Cs(y)FA(1-y)Pb(Br(x)I(1-)x)(3) while exposed to relative humidity cycles. We quantitatively compare three ML models while generating forecasts of environment-dependent PL responses: linear regression, echo state network, and seasonal autoregressive integrated moving average with exogenous regressor algorithms. We achieve accuracy of >90% for the latter, while tracking PL changes over a 50 h window. Samples with 17% of Cs content consistently showed a PL increase as a function of cycle. Our precise time-series forecasts can be extended to other HOIP families, illustrating the potential of data-centric approaches to accelerate material development for clean-energy devices.

Automated summary

The composition-dependent degradation of hybrid organic-inorganic perovskites (HOIPs) due to environmental stressors still hinders their commercialization. Machine learning models using high-throughput, in situ photoluminescence (PL) are implemented to predict the response of Cs(y)FA(1-y)Pb(Br(x)I(1-)x)(3) under relative humidity cycles. Accurate time-series forecasts with an accuracy of >90% are achieved, showing potential for data-centric approaches in accelerating material development for clean-energy devices.