用大模型+检索增强，快速发现高效低成本的高熵催化剂。
LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation
- 用检索增强生成框架让GPT-4访问5万+材料数据库，智能探索化学空间。
- 生成250+候选材料，82%热力学稳定，68%成本低于100元/公斤且性能达标。
- 比传统方法快200倍，适合材料、催化领域研究者加速创新。
CO2还原需要高效催化剂，但材料发现常需10至20年，依赖深厚专业知识。本文展示如何利用大语言模型辅助催化剂发现，通过检索增强的语义理解，使研究人员更高效地探索化学空间并解读结果。我们提出一种检索增强生成框架，让GPT-4能访问包含50,000多个已知材料的数据库，将通用语言理解能力转化为高通量材料设计工具。该方法生成超过250个催化剂候选物，其中82%具有热力学稳定性，68%满足成本低于100元/公斤、金属导电性（带隙<0.1eV）和机械稳定性（B/G>1.75）的多目标约束。最优催化剂Fe0.2Co0.2Ni0.2Ir0.1Ru0.3的极限电位为0.285V，较IrO2提升25%；而Cr0.2Fe0.2Co0.3Ni0.2Mo0.1在性能与成本间取得最佳平衡，成本仅为18元/公斤。火山图分析表明，78%的生成催化剂聚集于理论活性最优区附近。相比传统高通量筛选，本系统计算效率提升200倍。该工作证明，检索增强生成可在不牺牲探索性的前提下，将人工智能创意锚定于物理约束，实现自然语言接口下的高效材料发现流程，助力研究人员更高效地探索化学空间，并由大模型协助结果解读与假设生成。
原文摘要 · Abstract (English)
CO2 reduction requires efficient catalysts, yet materials discovery remains bottlenecked by 10-20 year development cycles requiring deep domain expertise. This paper demonstrates how large language models can assist the catalyst discovery process by helping researchers explore chemical spaces and interpret results when augmented with retrieval-based grounding. We introduce a retrieval-augmented generation framework that enables GPT-4 to navigate chemical space by accessing a database of 50,000+ known materials, adapting general-purpose language understanding for high-throughput materials design. Our approach generated over 250 catalyst candidates with an 82% thermodynamic stability rate while addressing multi-objective constraints: 68% achieved <$100/kg cost with metallic conductivity (band gap<0.1eV) and mechanical stability (B/G>1.75). The best-performing Fe0.2Co0.2Ni0.2Ir0.1Ru0.3 achieves 0.285V limiting potential (25% improvement over IrO2), while Cr0.2Fe0.2Co0.3Ni0.2Mo0.1 optimally balances performance-cost trade-offs at $18/kg. Volcano plot analysis confirms that 78% of LLM-generated catalysts cluster near the theoretical activity optimum, while our system achieves 200x computational efficiency compared to traditional high-throughput screening. By demonstrating that retrieval-augmented generation can ground AI creativity in physical constraints without sacrificing exploration, this work demonstrates an approach where natural language interfaces can streamline materials discovery workflows, enabling researchers to explore chemical spaces more efficiently while the LLM assists in result interpretation and hypothesis generation.
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