Universal descriptor for two-dimensional carbon nitrides based single-atom electrocatalysts towards nitrogen reduction reaction

Abstract

Electrocatalytic nitrogen reduction reaction (NRR) is widely regarded as one of the most promising ways for green and carbon-free ammonia (NH3) synthesis under mild conditions. Currently, the strategies of designing highly efficient, selective, and stable NRR electrocatalysts is desirable. The two-dimensional (2D) carbon nitrides (CNx) with different C/N ratios (e.g., g-C2N, g-CN, g-C3N4, g-C4N3, g-C9N4) have been widely explored as the substrates for single-atom catalysts (SACs) towards NRR. Through density functional theory (DFT) calculations, the NRR electrocatalytic activity of the single transition metal (TM) atom anchored CNx are systematically investigated and revisited. Based on evaluations of stability and catalytic activity, six promising NRR electrocatalysts are screened out, with considerably low limiting potential (UL). Most importantly, we employed a machine learning (ML) based screening strategy aimed at predicting efficient NRR electrocatalysts by constructing the universal structure-activity descriptor, which encompasses both the intrinsic properties of TM atom and the 2D CNx with various C/N ratios. The successful application of the newly proposed descriptor on the new system TM@C8N8 validates its universality. Our research holds promise for providing theoretical guidance in synthesizing highly active NRR electrocatalysts.