使用大型语言模型合成高度结晶的共价有机框架
Kaiyu Wang1,2,3, Daehyun Daniel Ahn1,2,3, Nakul Rampal1,2,3
1Department of Chemistry, University of California, Berkeley, California 94720, United States.
Journal of the American Chemical Society
|February 23, 2026
概括
现在可以使用人工智能驱动的方法加速共价有机框架 (COF) 结晶. 这种方法,即LLM For Accelerated Synthesis Technique (LFAST),可以将合成时间从几年缩短到一个月以下.
科学领域:
- 材料科学 材料科学 材料科学
- 化学 化学 化学
- 人工智能的人工智能
背景情况:
- 结晶共价有机框架 (COF) 对网状化学至关重要,但往往需要广泛的优化.
- 在COF中实现远程订单通常涉及漫长的试错过程,涉及几个月或几年.
研究的目的:
- 显著加速共价有机框架的结晶过程.
- 为了减少COF合成所需的时间,并改善结构秩序.
主要方法:
- 在ChatGPT中集成一个深度研究代理,以形成LLM For Accelerated Synthesis Technique (LFAST).
- 使用结构化,多步骤提示,从化学文献中挖掘和验证合成参数.
- 使用具有高通量粉末X射线衍射 (PXRD) 的自动化合成平台进行条件执行和分析.
主要成果:
- 将COF结晶时间缩短到不到一个月.
- 在基准COF,TpPa-SO3H的晶度指数 (CI) 中实现了350%的增加.
- 成功合成了一种未报告的COF-2000,具有增强的结构顺序.
结论:
- 采用LFAST方法极大地加快了COF结晶的速度,并提高了材料质量.
- 引入了一个标准化的元数据格式,以提高可复制性和数据可访问性.
- 这种数据驱动的方法改变了COF合成,加速了材料的发现.
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