Data‐driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts

Design of heterogeneous catalysts is necessarily surface-focused, generally achieved via adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface however is practically challenging because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this minireview we summarize recent progress in using machine-learning to search-out and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments and, identify and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and benefit researchers in optimal design of heterogeneous catalysts.