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

Abstract

The 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, 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-C₂N, g-CN, g-C₃N₄, g-C₄N₃, and g-C₉N₄) 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@C₈N₈ validates its universality. Our research holds promise for providing theoretical guidance in synthesizing highly active NRR electrocatalysts.