Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

Perovskite photovoltaics are efficient and inexpensive, yet their performance is dynamic. In this Perspective, we examine the effects of H 2 O, O 2 , bias, temperature, and illumination on device performance and recovery. First, we discuss pivotal experiments that evaluate perovskites' ability to go through a reap-rest-recovery (3R) cycle, and how machine learning (ML) can help identify the optimum values for each operating parameter. Second, we analyze perovskite dynamics and degradation, emphasizing the research challenges surrounding this 3R cycle. We then outline experiments that could identify the impact of environmental factors on recovery for different perovskite compositions. Finally, we propose an ML paradigm for maximizing long-term performance and predicting device performance recovery, including a shared-knowledge repository. By reframing perovskites' optoelectronic transiency within the context of recovery rather than degradation, we highlight a set of research opportunities and the artificial intelligence solutions needed for the commercial adoption of these promising solar cell materials.