Advances in computational approaches for bridging theory and experiments in electrocatalyst design

molecular dynamics and machine learning-accelerated molecular dynamics, has significantly advanced our understanding of the dynamic electrochemical interface. High-throughput computational workflows and data-driven machine learning techniques have further streamlined catalyst discovery by efficiently exploring large material spaces and complex reaction pathways. Together, these computational advances not only provide mechanistic insights into inert molecule activation but also offer a robust platform for guiding experimental efforts. The review concludes with a discussion of remaining challenges and future opportunities to further integrate computational and experimental methodologies for the rational design of next-generation electrocatalysts.