A closed-loop framework for perovskite precursor additive discovery

Efficient discovery of precursor additives is essential for improving the performance of perovskite solar cells, yet the large chemical space makes conventional trial-and-error screening inefficient. Here we show that LEAP (LLM-driven Exploration via Active Learning for Perovskites), a closed-loop framework combining a domain-specialized language model with active learning, prioritizes perovskite precursor additives under low-data conditions. The model extracts mechanism-relevant knowledge from the literature and converts it into interpretable molecular descriptors for uncertainty-aware Bayesian optimization. On an evaluation drawn from unseen literature, the specialized model provides more mechanism-consistent answers than the tested general-purpose models. In an expert-guided proof-of-concept study, two later-round additives increase average device power conversion efficiencies to 20.13% and 20.87%, compared with 19.25% for the control, and the best device reaches 21.32%. Because only a limited number of candidates are tested prospectively, these results provide preliminary evidence that literature-grounded molecular representations, Bayesian optimization, and expert assessment can support additive discovery for perovskite solar cells. Discovering effective perovskite precursor additives is challenging due to the vast chemical space. Here, the authors present a closed-loop active-learning framework that leverages literature knowledge to accelerate perovskite precursor additive discovery, achieving solar cell efficiencies up to 21.32%.