LEAP: 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. We develop LEAP (LLM-driven Exploration via Active Learning for Perovskites), an expert-in-the-loop closed framework that couples a domain-specialized large language model with active learning for iterative additive prioritization. Experimental validation shows improved additive prioritization, leading to average device PCEs of 20.13% and 20.87% for later-round additives, compared with 19.25% for control, and a champion PCE of 21.32%.
[2605.20242] LEAP: A closed-loop framework for perovskite precursor additive discovery
[Submitted on 18 May 2026]
Title:LEAP: A closed-loop framework for perovskite precursor additive discovery
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Abstract: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. We develop LEAP(LLM-driven Exploration via Active Learning for Perovskites), an expert-in-the-loop closed framework that couples a domain-specialized large language model(LLM) with active learning for iterative additive prioritization. The LLM is trained to extract mechanism-relevant knowledge from the perovskite additive literature and to represent candidate molecules through interpretable descriptors, which are further integrated into a Bayesian optimization workflow for uncertainty-aware prioritization under low-data conditions. Benchmark results on unseen literature show that the domain-specialized model outperforms general-purpose models in mechanism-consistent reasoning. Experimental validation in an expert-in-the-loop proof-of-concept study suggests improved additive prioritization across three screening rounds, leading to average device PCEs of 20.13% and 20.87% for the later-round 6-CDQ- and 2-CNA-treated devices, respectively, compared with 19.25% for the control, with a champion PCE of 21.32%. These results provide preliminary evidence that literature-grounded mechanistic descriptors, when coupled with Bayesian optimization and expert feasibility review, can support mechanism-aware additive prioritization in perovskite photovoltaics.
Comments: 30 pages; 11 figures
Subjects:
Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph)
Cite as: arXiv:2605.20242 [cs.LG]
(or arXiv:2605.20242v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.20242
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xin-De Wang [view email] [v1] Mon, 18 May 2026 02:50:37 UTC (6,055 KB)
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