In Angewandte Chemie (International ed. in English)
Numerous activity descriptors have been developed to rationally design single-atom catalysts (SACs), such as energy-based or electronic descriptors. However, their computationally costly and experimentally unobtainable properties limit the understanding of the structure-activity relationship to a case-by-case basis. This paper describes a simple and interpretable descriptor directly related to activity, which can be not only easily obtained from the atomic databases, but also experimentally verified by X-ray absorption spectroscopy. The defined descriptor proves to accelerate high-throughput screening of more than 700 graphene-based SAC models 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 intrinsic relations of various crucial features in the complicated metal-ligand interaction, thus the structure-activity relationship is clarified 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. Several other new advanced catalysts are also identified. Orderly combining machine learning with physical insights, this work provides a new generalized strategy for crossing the chasm between low-cost high-throughput screening and a comprehensive understanding of the structure-mechanism-activity relationship.
Lin Xiaoyun, Wang Yongtao, Chang Xin, Zhen Shiyu, Zhao Zhi-Jian, Gong Jinlong
2023-Mar-09
intrinsic descriptor * high-throughput screening * structure-activity relationship * single-atom catalysts * machine learning
