To mitigate perovskites’ degradation, there have been a pressing need to identify the effects of environmental stressors on material physical behavior and device performance. We implement high-throughput environmental photoluminescence (PL) to interrogate the response of Cs-FA perovskites with a range of chemical composition while exposed to temperature and relative humidity cycles. These measurements are used as input when comparing how machine learning methods can be realized to forecast material response. We quantitatively compare linear regression, Echo State Network (ESN), and Auto-Regressive Integrated Moving Average with eXogenous regressors (ARIMAX).
Low-cost, high-efficiency metal halide perovskite solar cells (PSC) are a promising alternative to Si photovoltaics, but poor stability currently precludes commercialization. We present a framework for accelerated PSC design using machine learning (ML) to identify optimal compositions, fabrication parameters, and device operating conditions. We present four examples showcasing our ML roadmap using various types of neural networks, applied to diverse problems such as forecasting time-series photoluminescence (PL) from perovskite thin films, projecting PSC power output and degradation over time, and predicting figures of merit from simple, high-throughput experimental procedures. Our paradigm informs the rational development of perovskite devices, providing an accelerated pathway to commercialization.
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