Mechanistic insights and rational catalyst design in NO x electroreduction

RR), emphasizing mechanistic insights into reaction pathways, key intermediates, and activity-determining descriptors. We highlight the role of computational modeling, from density functional theory (DFT) studies and microkinetic simulations to machine learning-driven approaches, in elucidating active sites, guiding rational catalyst design, and accelerating material discovery. Special attention is given to the emerging synergy between theory and experiment, which bridges idealized models and realistic electrochemical conditions, thereby enabling data-driven catalyst discovery and mechanism-guided design. Finally, we outline the remaining challenges and future directions, emphasizing innovations in computational techniques and scalable catalyst development for sustainable ammonia synthesis and nitrogen waste reduction.