赋予化学专家的大型语言模型的权力,以在单原子催化中进行文献解释,以实现先进的氧化
Jing-Hang Wu1,2, Ran Shi1, Xiao Zhou1,2
1State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, 230026, China.
Angewandte Chemie (International ed. in English)
|October 24, 2025
概括
大型语言模型 (LLM) 为催化剂设计提取数据,但专家的意见对于可靠的化学知识至关重要. 人在循环中的工作流确保从LLM输出获得准确的洞察力.
科学领域:
- 催化剂是一种催化剂.
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 大型语言模型 (LLM) 提供了从科学文献中提取数据的潜力.
- 自动数据提取往往缺乏可靠化学知识所需的领域专业知识.
- 单原子催化剂 (SAC) 在高级氧化过程 (AOP) 中至关重要.
研究的目的:
- 开发一个人-在-循环工作流程,以整合LLM-便利的数据提取与专家策划.
- 在高级氧化过程 (AOP) 中使用单原子催化剂 (SAC) 来证明工作流程的有效性.
- 揭示催化剂特性和性能之间的相关性,以获得机械学的见解.
主要方法:
- 从科学文献中提取基于LLM的结构化数据.
- 由专家指导的代策划和分析提取的数据.
- 人类驱动的快速改进和模型比较.
- 用于解释结构-性能关系的统计分析.
主要成果:
- 有效的数据提取和对AOPs的SACs的严格策划.
- 确定金属类型,协调环境,反应物质和催化性能之间的关键相关性.
- 对SAC驱动的AOP进行更深入的机制性洞察.
结论:
- 人在循环中的方法优于完全自动化的方法,用于将LLM输出转化为可靠的科学知识.
- 人类专业知识是解释LLM生成的数据和获得有意义的催化见解的核心.
- 工作流有效地弥合了结构化数据和催化相关理解之间的差距.
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