Lin Xiaoyun, Wang Yongtao, Chang Xin, Zhen Shiyu, Zhao Zhi-Jian, Gong Jinlong
Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology, Tianjin University, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Weijin Road 92, 300072, Tianjin, China.
Haihe Laboratory of Sustainable Chemical Transformations, 300192, Tianjin, China.
Angew Chem Int Ed Engl. 2023 May 2;62(19):e202300122. doi: 10.1002/anie.202300122. Epub 2023 Apr 3.
Developing easily accessible descriptors is crucial but challenging to rationally design single-atom catalysts (SACs). This paper describes a simple and interpretable activity descriptor, which is easily obtained from the atomic databases. The defined descriptor proves to accelerate high-throughput screening of more than 700 graphene-based SACs without computations, universal for 3-5d transition metals and C/N/P/B/O-based coordination environments. Meanwhile, the analytical formula of this descriptor reveals the structure-activity relationship at the molecular orbital level. Using electrochemical nitrogen reduction as an example, this descriptor's guidance role has been experimentally validated by 13 previous reports as well as our synthesized 4 SACs. Orderly combining machine learning with physical insights, this work provides a new generalized strategy for low-cost high-throughput screening while comprehensive understanding the structure-mechanism-activity relationship.
开发易于获取的描述符对于合理设计单原子催化剂（SAC）至关重要，但具有挑战性。本文描述了一种简单且可解释的活性描述符，它很容易从原子数据库中获得。所定义的描述符被证明可以加速对700多种基于石墨烯的单原子催化剂的高通量筛选，无需计算，对3-5d过渡金属以及基于C/N/P/B/O的配位环境具有通用性。同时，该描述符的解析公式揭示了分子轨道水平上的结构-活性关系。以电化学氮还原为例，该描述符的指导作用已被之前的13篇报道以及我们合成的4种单原子催化剂通过实验验证。将机器学习与物理见解有序结合，这项工作为低成本高通量筛选提供了一种新的通用策略，同时全面理解结构-机理-活性关系。
