Toward accelerated electrocatalyst design: synergistic integration of DFT, machine learning, and microkinetic modeling

RR), and nitrogen reduction reaction (NRR) are highlighted. Finally, current challenges-including data quality, descriptor selection, model transferability, interpretability, realistic electrochemical modeling, and multiscale integration-are critically assessed. Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.