化学家指导的人类-人工智能工作流程,用于共价有机框架合成.
Lihan Chen1, Zhen Lu2, Lin Chen2
1College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.
Journal of the American Chemical Society
|February 11, 2026
概括
这项研究引入了一个人-人工智能工作流程,以优化共价有机框架 (COF) 合成,超越试错. 人工智能系统提高了COF材料设计中溶剂和催化剂选择的预测准确度.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 化学合成 化学合成
背景情况:
- 共价有机框架 (COF) 合成严重依赖专家直觉和代实验,对可扩展性和可重复性构成挑战.
- 目前的方法缺乏合理设计的系统方法,阻碍了对新型COF材料的有效发现.
研究的目的:
- 开发化学家指导的人类-人工智能工作流程,以数字化和优化COF合成推理循环.
- 为了提高对COF的最佳合成条件 (溶剂,催化剂,温度,时间,静电量) 的预测.
- 为了使复杂的网状材料的合理设计和可扩展的合成.
主要方法:
- 从800多个出版物 (2709个协议) 中构建了一个COF合成知识库.
- 结合了结构化的文献知识库和检索增强的大型语言模型来生成假设.
- 集成了一个基于实验的诊断模块,使用宏观观测和粉末X射线衍射 (PXRD) 进行代更新.
主要成果:
- 人类-人工智能工作流显示,在60个持有COF的离开一个out基准中,溶剂-催化剂命中率提高到0.83.
- 成功合成了两个含的COF (TAPPy-4F和TAPPy-8F),表现出结晶性和永久性多孔性.
- 该系统显示强大的可转移性超出单个案例研究,验证其通用性.
结论:
- 开发的人类-人工智能工作流提供了一个可泛化和可扩展的范式,用于COF的合理设计.
- 这种方法将专家知识与以模型驱动的探索相结合,加速了材料的发现.
- 该系统有效地模拟了化学家的推理循环,提高了合成效率和可重复性.
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