机器学习揭示了由金属有机框架催化剂进行选择性C─H玻利的内腔与表面活动
Zhaomin Su1, Bingling Dai1, Xue Wang1
1iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, 422 South Siming Rd., Siming District, Xiamen, Fujian, 361005, P.R. China.
Angewandte Chemie (International ed. in English)
|May 7, 2025
概括
机器学习确定了金属有机框架 (MOF) 催化剂的关键因素,用于选择性C-H化. 这使得具有高sp3和sp2选择性的MOF支持催化剂的合理设计成为可能.
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:
- 金属有机框架 (MOF) 是用于异质催化的多功能平台.
- 在MOF中区分孔封闭和表面催化是具有挑战性的.
- 了解结构-活动关系对于设计高效的MOF催化剂至关重要.
研究的目的:
- 为了阐明选择性C-H化中MOF支持的 (Ni) 催化剂的结构-活性关系.
- 为了确定控制 sp3 与 sp2 C-H 化选择性的关键因素.
- 为合理的MOF催化剂设计开发一个系统的框架.
主要方法:
- 可解释机器学习应用于超过47万个MOF结构.
- 为MOF结构开发了45个具有化学意义的描述符.
- 对sp3和sp2化不同的激活机制 (HAT与CMD) 的分析.
主要成果:
- 确定了影响MOF催化剂选择性的关键结构描述因素.
- 揭示了sp3 (毛孔中的根性HAT) 和sp2 (表面/缺陷上的CMD) 化的独特机制.
- 使用设计的Ni催化剂,实现了对sp3 (高达97.8%) 和sp2 (高达88.7%) 化的高选择性.
结论:
- 在MOF支持的催化中,为控制活动偏好建立了可通用的原则.
- 在催化剂设计中展示了可解释机器学习的力量.
- 为选择性C-H功能化MOF催化剂的合理设计提供了一个系统的框架.
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