Application of machine learning interatomic potentials in heterogeneous catalysis

Abstract

Heterogeneous catalysts are crucial in modern societies as they promote sustainability by enabling lower-energy pathways for various chemical reactions. While Density Functional Theory (DFT) computations can provide critical insights into how heterogeneous catalysts operate at the atomic level, they are limited by computational costs and unfavorable scaling with system size. Recently, machine learning interatomic potentials (MLIPs) have emerged as a promising alternative to DFT, offering near-DFT accuracy at significantly reduced cost. Here, in this perspective, we discuss the application of MLIPs in heterogeneous catalyst modeling as a surrogate for DFT. We detail how MLIPs have been applied in thermal catalysis to probe active sites, enable studying complex metallic and nanoporous catalysts, and investigate the reconstruction of catalytic surfaces. We review the use of MLIPs in electrocatalysis and photocatalysis, emphasizing their capabilities in studying transition metal oxide surfaces and solid–liquid interfaces. We also discuss the current limitations of MLIPs, particularly their challenges with transferability and description of non-local interactions. Finally, we conclude by identifying promising and underexplored domains in which MLIPs can further advance our understanding of heterogeneous catalysts.