“Aggregation and rebalance” mechanism-guided design and discovery of efficient bimetallic catalysts for the nitrogen reduction reaction

Abstract

Dual-atom catalysts (DACs), which function as replacements for precious metal catalysts, have been demonstrated to enhance the efficiency of the electrocatalytic nitrogen reduction reaction (NRR) with greater efficacy than single-atom catalysts (SACs). However, when compared to SACs, the research on DACs presents a higher degree of complexity, accompanied by a multitude of influencing factors that govern their catalytic mechanisms. Consequently, gathering a vast amount of data is necessary. As such, the task of exploring the impact of charge transfer mechanisms within DACs on catalyst performance remains a formidable challenge. Herein, based on density functional theory (DFT) calculations, an “aggregation and rebalance” mechanism is proposed for 45 combinations of bimetallic pair doped BC₆N nanosheets (TM_ATM_B@BC₆N) to reveal the effect of the combined action of the intrinsic features of the active center and the coordination atoms on the catalyst activity. After multi-step screening, CrCr@BC₆N and CrMo@BC₆N exhibit excellent performance with limiting potentials of 0 and −0.06 V, respectively. The DFT-derived data serve as the training set for five machine learning algorithms used in training and importance analysis, and the most accurate algorithm among them is employed to predict 842 potential efficient DACs. Finally, several representative catalysts are selected for DFT calculations to verify the accuracy of the predicted performance and the correctness of the proposed mechanism. This work offers novel insights into the design of DACs and further refines the theory related to the charge transfer mechanism in DACs.