摘要
In this review, we describe a paradigm shift in additive discovery for perovskite solar cells (PSCs) from trial-and-error methods to AI-driven approaches, covering the physicochemical foundations of additive engineering, machine-learning descriptors, active learning optimization, generative models, and the emerging trend of integrating large language models with autonomous laboratories for closed-loop autonomous discovery.