Advancing electrocatalysis through density functional theory: From reaction mechanisms to machine learning integration

This review examines the pivotal role of density functional theory (DFT) in advancing electrocatalysis research, which underpins sustainable energy technologies ranging from fuel cells to water electrolyzers. We provide a comprehensive analysis of how DFT calculations elucidate reaction mechanisms at atomic and electronic levels, particularly for environmentally critical processes such as hydrogen peroxide production, oxygen reduction, hydrogen evolution, and carbon dioxide reduction. The review details how DFT investigations have revealed the influence of crystal facet engineering, atomic coordination environments, and electronic structures on catalytic performance. Despite its utility, conventional DFT faces inherent limitations when modeling semiconductor catalysts and complex electrochemical interfaces. We discuss innovative methodologies developed to overcome these challenges, including the integration of Poisson equations to model band bending, catalytic unit engineering approaches, and Grand-Canonical DFT frameworks. Additionally, we explore the emerging synergy between DFT and machine learning, which is revolutionizing catalyst discovery through high-throughput screening and accelerated optimization of novel materials. This integration enables researchers to navigate vast chemical spaces and establish structure-property relationships with unprecedented efficiency. Future advances in electrocatalysis will depend on further interdisciplinary collaboration, combining expertise in computational chemistry, materials science, and artificial intelligence to design highly efficient and stable catalytic systems that address pressing environmental and energy challenges. • Review on Density Function Theory (DFT) used in electrocatalysis. • Studies DFT on electrocatalysis involving H 2 O 2 production are discussed. • Studies using DFT on other electrocatalysis are discussed. • Strategies directly applicable to combination of DFT and Machine learning are discussed.