大型语言模型引导的自动化反应途径探索
Ruzhao Chen1, Yubang Liu1, Zhe Chen1
1School of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China.
Communications chemistry
|August 23, 2025
概括
我们开发了ARplorer, 一个用于快速自动化反应路径探索的新程序. 这种工具有助于研究反应机制,并加速数据驱动的催化剂设计.
科学领域:
- 计算化学
- 化学反应动力学
- 催化剂
背景情况:
- 对反应途径的自动探索对于理解反应机制至关重要.
- 数据驱动的方法需要有效的反应开发和催化剂设计方法.
研究的目的:
- 提出一个新的程序, 自动化, 快速, 有效的反应途径的探索.
- 加强对潜在能量表面 (PES) 的研究,加快反应发现.
主要方法:
- 量子力学和基于规则的方法的整合.
- 使用大型语言模型辅助的化学逻辑.
- 实施活跃学习以进行过渡状态采样和并行多步反应搜索与过.
主要成果:
- 在自动化反应路径搜索方面表现出有效性和多功能性.
- 对各种案例研究的成功应用:有机循环添加,不对称的Mannich类型和Pt催化反应.
- ARplorer可用于反应开发和催化剂设计的高吞吐量选.
结论:
- ARplorer为自动化反应途径探索提供了一个有效的工具.
- 该计划显著增强了数据驱动的反应开发和催化剂设计.
- 它的可扩展性支持高通量选应用程序.
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