Machine Learning Guided d‐Orbital Electron Modulated Graphene‐Based Catalysts for Enhanced Electrochemical Ammonia Synthesis

The selective reduction of atmospheric nitrogen to ammonia under ambient conditions via electrochemical methods has emerged as a promising alternative to the Haber–Bosch process. Despite its feasibility, the performance of core catalysts has been constrained by the strength of the π‐backdonation between the d‐orbital electrons of the metal active center and the antibonding orbitals of nitrogen. In this study, we propose constructing M‐N 3 structures and introducing auxiliary metals to cooperatively regulate the local crystal field to enhance the π‐backdonation and promote nitrogen activation. By employing machine learning (ML) to analyze the electronic structure and using the number of d electrons and electronegativity of the metal as key descriptors, we successfully established a quantitative relationship between the π‐backdonation strength, catalytic activity and identified tungsten and molybdenum as high‐performance candidate metals. The corresponding graphene‐based catalysts were successfully prepared experimentally, with the tungsten‐based catalyst achieving an ammonia production rate of 150.08 μg h −1 mg cat −1 and a Faraday efficiency of 85.7% at −0.9 V vs. RHE. Density functional theory (DFT) calculations jointly confirmed that the strategy of regulating the local crystal field effectively optimized the d‐orbital energy level splitting and electron occupation, promoting the formation of the π‐backdonation. This work demonstrates the effectiveness of the crystal field engineering strategy in modulating d‐orbital electrons through machine learning and DFT calculations and confirms the unique guiding role of machine learning in the reverse design of high‐performance electrocatalysts.