High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor
Xiaoyun Lin, Yongtao Wang, Xin Chang, Shiyu Zhen, Zhi‐Jian Zhao, Jinlong Gong
Abstract
Abstract 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.