High-Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor

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.

Automated summary

This paper proposes a simple and interpretable activity descriptor, which can be easily obtained from atomic databases, to accelerate high-throughput screening of more than 700 graphene-based single-atom catalysts. It is universal for 3-5d transition metals and C/N/P/B/O-based coordination environments. The analytical formula of this descriptor reveals the structure-activity relationship at the molecular orbital level. Experimental validation and synthesis of 4 single-atom catalysts confirm the guidance role of this descriptor in electrochemical nitrogen reduction. This work combines machine learning and physical insights, providing a new generalized strategy for low-cost high-throughput screening and comprehensive understanding of the structure-mechanism-activity relationship.