Integrating Machine Learning With Constant‐Potential Simulation to Unravel Charge‐Transfer Mechanisms in Electrochemical Nitrogen Fixation

ABSTRACT The electrochemical nitrogen reduction reaction (NRR) offers a sustainable approach to ammonia (NH ₃ ) synthesis under mild conditions. To achieve scalable NH ₃ production, discovering high‐performance catalysts for the efficient NRR is crucial. For this purpose, the activity mechanisms of functional group‐modified carborin/graphene‐supported single‐atom catalysts were systematically investigated using the grand‐canonical fixed‐potential method, which simulates operando constant‐potential conditions. Among 144 candidates screened, Cr@NO ₂ ‐carborin/graphene and Cr@CHO‐carborin/graphene are identified as the most promising NRR catalysts, with low limiting potentials of −0.220 V for the ^* N ₂ → ^* N ₂ H step and −0.245 V for the ^* NH→ ^* NH ₂ step, respectively. Furthermore, interpretable machine learning models revealed that the shift in potential of zero charge, induced by intermediate adsorption, serves as the key voltage‐responsive descriptor governing charge transfer patterns and influencing the N ₂ activation. These findings establish a paradigm shift from static electronic descriptors to dynamic interfacial property engineering, offering a universal framework for designing electrocatalysts for multi‐electron reactions like NRR.