Bridging Computation and Experiment in Electrocatalyst Active Site Discovery: Challenges and Opportunities

Abstract Integrating computational screening of catalyst structures with experimental validation offers exciting opportunities in searching for active, selective, and durable electrocatalysts for reactions ranging from hydrogen production to green fuel synthesis. Despite substantial advances on both fronts, however, the translation of theoretical predictions into experimentally realized catalysts remains challenging. We argue that this gap does not just reflect shortcomings in theory or experimental approaches but also the insufficient emphasis on explicitly defining, synthesizing, and validating electrocatalytic active sitesthe fundamental unit of catalytic properties. Traditional discovery strategies often attempt to achieve two goalsdiscovering efficient active sites and developing all-around application-ready catalystsin one step, which substantially complicates progress on both fronts. In this perspective, we propose that elevating the explicit identification and validation of intrinsic active-site structures to a central objective can substantially strengthen theory−experiment integration for discovering promising active sites. We review recent progress enabled by combined computational and experimental approaches, critically assess key bottlenecks that limit predictive discovery capability, and highlight emerging opportunities to establish feedback-driven workflows centered on explicit active-site hypotheses. By reframing active-site discovery as a deliberate design and validation task, we outline a pathway toward more reliable and predictive electrocatalyst discovery.