A universal descriptor for two-dimensional carbon nitride-based single-atom electrocatalysts towards the nitrogen reduction reaction

The electrocatalytic nitrogen reduction reaction (NRR) is widely regarded as one of the most promising ways for green and carbon-free ammonia (NH 3 ) synthesis under mild conditions. Currently, strategies for designing highly efficient, selective, and stable NRR electrocatalysts are desirable. Two-dimensional (2D) carbon nitrides (CN x ) with different C/N ratios ( e.g. , g-C 2 N, g-CN, g-C 3 N 4 , g-C 4 N 3 , and g-C 9 N 4 ) have been widely explored as substrates for single-atom catalysts (SACs) towards the NRR. Through density functional theory (DFT) calculations, the NRR electrocatalytic activity of the single transition metal (TM) atom anchored CN x is systematically investigated and revisited. Based on evaluations of stability and catalytic activity, six promising NRR electrocatalysts are screened out, with considerably low limiting potential ( U L ). Most importantly, we employed a machine learning (ML)-based screening strategy aimed at predicting efficient NRR electrocatalysts by constructing a universal structure-activity descriptor, which encompasses both the intrinsic properties of the TM atom and the 2D CN x with various C/N ratios. The successful application of the newly proposed descriptor on the new system TM@C 8 N 8 validates its universality. Our research holds promise for providing theoretical guidance in synthesizing highly active NRR electrocatalysts. A universal descriptor was constructed by combining DFT calculations and machine learning to predict highly active NRR electrocatalysts based on transition metal atom anchored 2D carbon nitrides with varied C/N ratios (TM@CN x ).