LEAP: A closed-loop framework for perovskite precursor additive discovery
The article discusses LEAP, a closed-loop framework designed for the discovery of perovskite precursor additives. This framework integrates a specialized large language model with active learning to enhance the efficiency of additive prioritization. Preliminary results indicate that this approach can significantly improve the performance of perovskite solar cells compared to traditional methods.
- ▪LEAP combines a domain-specialized large language model with active learning for effective additive discovery.
- ▪The framework utilizes Bayesian optimization for uncertainty-aware prioritization in low-data conditions.
- ▪Experimental validation shows improved device performance, with average power conversion efficiencies of 20.13% and 20.87% in later rounds.
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Computer Science > Machine Learning arXiv:2605.20242 (cs) [Submitted on 18 May 2026] Title:LEAP: A closed-loop framework for perovskite precursor additive discovery Authors:Xin-De Wang, Zhi-Rui Chen, Ze-Feng Gao, Peng-Jie Guo, Cheng Mu, Zhong-Yi Lu View a PDF of the paper titled LEAP: A closed-loop framework for perovskite precursor additive discovery, by Xin-De Wang and 5 other authors View PDF HTML (experimental) 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.
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