arXiv:2605.20242v1 Announce Type: new 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
The convergence of advanced AI capabilities, particularly LLMs and active learning, with the urgent need for more efficient material discovery in renewable energy, makes this development timely.
This development portends a significant acceleration in materials science R&D, specifically in areas critical for energy transition like solar cell efficiency, by largely automating and optimizing the discovery process.
The conventional trial-and-error approach to materials discovery is being replaced by an AI-driven, expert-in-the-loop system, markedly speeding up the identification and optimization of complex chemical formulations.
- · Renewable energy sector
- · AI/ML companies
- · Materials science researchers
- · Chemical manufacturers
- · Traditional R&D methodologies
- · Companies slow to adopt AI in R&D
Perovskite solar cell efficiency improves at an accelerated rate.
Broader application of AI-driven material discovery frameworks across other complex material challenges, such as catalysts or batteries.
Reduced cost of renewable energy and increased global energy independence due to faster innovation cycles in key energy technologies.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG