Computational modeling of electrocatalyst reconstruction

Abstract

Electrocatalyst reconstruction under operating conditions is now widely recognized as a key factor governing catalytic activity, selectivity, and stability. Substantial progress has been made in developing computational methods to describe this structurally dynamic behavior, ranging from density functional theory-based phase diagram analysis to global structure search, ab initio molecular dynamics, kinetic Monte Carlo, and machine learning-accelerated simulations. In this Review, we provide a concise overview of how these approaches have advanced the mechanistic understanding of electrocatalyst reconstruction across multiple time and length scales. We highlight how different computational strategies offer complementary insights into thermodynamically accessible states, atomic-scale restructuring pathways, and kinetically relevant structural evolution under reaction conditions. We emphasize the recent progress in machine learning potentials and multiscale modeling frameworks, which are extending simulations towards increasingly realistic chemical complexity. We also discuss the key challenges that remain, especially the need for open-system models that explicitly account for interfacial exchange, dissolution–redeposition processes, and the coupling between the local chemical environment and structural dynamics. This Review aims to provide a practical guide to the current computational toolbox and to outline directions for the next generation of predictive modeling for electrocatalyst reconstruction.