Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

High-performing and low-cost photovoltaics (PV) are critical to the continued adoption of renewable energy sources. While promising, perovskite solar materials show a dynamic optoelectronic response when exposed to H₂O, O₂, bias, temperature, or light that severely impacts their performance, preventing commercialization. We posit a reap-rest-recovery cycle to avoid permanent material degradation and achieve long-term power conversion efficiency through machine learning (ML). First, the influence of each above-mentioned parameter must be investigated individually and in combination, from the nano- to the macroscale. With sufficient data for ML, provided by a shared-knowledge repository, monitoring frameworks for perovskite solar cells will be developed to maximize long-term operation by using predictive methods to determine the ideal pathways to recovery through rest. With these milestones achieved, we expect perovskite PV to reach the 25 years T₈₀ lifetime requirement.