High‐Throughput Design of Efficient Dual‐Atom Catalysts Anchored on C 2 N for Electroreduction of CO and NH 3 to Acetamide via Atomic Modeling and Machine Learning

Electrocatalytic C–N coupling technology provides a new approach for green acetamide synthesis, which is achieved via direct reduction of carbon monoxide (CO) and ammonia (NH 3 ). However, efficient, stable catalysts urgently need to be developed, and their reaction mechanisms elucidated. In this study, density functional theory is employed to systematically screen a series of homonuclear (TM 2 ‐C 2 N) and heteronuclear (CuTM‐C 2 N) copper‐based transition metal (TM) dual‐atom catalysts (DACs). After comprehensively evaluating their stability, catalytic activity, and selectivity, five candidates with excellent performance are identified: Cu 2 –C 2 N, CuTi–C 2 N, CuV–C 2 N, CuCr–C 2 N, and CuMn–C 2 N. The limiting potential for acetamide synthesis over the DACs is linearly correlated with the Gibbs free‐energy change (Δ G ) of the *CO → *CHO step. Exploring the intrinsic relationships between the binding energies of different intermediates reveals a linear scaling relationship, indicating their correlation and potential to limit catalytic performance. The synergistic effect between heteronuclear TM pairs successfully overcomes this inherent limitation, significantly reducing the Gibbs free energy difference between the *CO and *CHO intermediates. Machine learning is adopted to explore the structure–activity relationship of the identified DACs, and the sure independence screening and sparsifying operator method is employed to construct a robust descriptor system, enabling accurate prediction of the maximum uphill Gibbs free‐energy change among the elementary steps of a given reaction pathway (Δ G max , eV). This study elucidates the reaction mechanism underlying the electrocatalytic synthesis of acetamide from CO and NH 3 , providing theoretical guidance for highly efficient DAC design.