协同利用知识图和大型语言模型进行中继催化路径推
Fei Fu1, Qing-Qing Li1, Fangrong Wang2
1State Key Laboratory of Physical Chemistry of Solid Surface, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
National science review
|August 27, 2025
概括
研究人员开发了一个自动化系统,使用知识图和大型语言模型来发现新的中继催化途径. 这种人工智能驱动的方法显著加快了有效的多级催化反应的识别.
科学领域:
- 催化剂
- 化学工程
- 计算化学
背景情况:
- 继电催化能够实现高效的多步转换,但设计路径是劳动密集的.
- 目前的方法严重依赖于专家文献分析,限制了发现速度和范围.
研究的目的:
- 开发一种自动化的方法来推多步继电催化路径.
- 加速设计和发现新的催化反应序列.
主要方法:
- 整合知识图 (KG) 和大型语言模型 (LLM) 进行路径推.
- 通过LLM辅助的数据采集和组织来构建一个全面的催化知识图 (Cat-KG).
- 评分规则和LLM驱动的文本生成用于路径验证和呈现.
主要成果:
- 该方法成功地在几分钟内转换了乙烯,乙醇和2,5-碳酸盐的催化途径.
- 确定的途径与报告的途径一致,表明有效性和新发现的潜力.
- 该系统生成可读化学方程式和描述,整合可靠的催化知识.
结论:
- 联合KG和LLM策略自动化了继电催化路径的发现.
- 这种方法大大减少了设计复杂催化反应的时间和成本.
- 该战略显示出可以推断已知的并确定新的继电催化途径的潜力.
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