Rational Design of Water Splitting Electrocatalysts through Computational Insights

Abstract

Electrocatalytic water splitting is vital for the sustainable production of green hydrogen. Electrocatalysts, including those for the hydrogen evolution reaction at the cathode and the oxygen evolution reaction at the anode, are crucial in determining the overall performance of water splitting. Traditional methods to electrocatalyst development often relied on trial-and-error, which can be time-consuming and inefficient. Recent advancements in computational techniques provide more systematic and predictive strategies for catalyst design. This review article explores the role of computational insights in the development of water-splitting electrocatalysts. We start by giving an introduction of electrocatalytic water splitting mechanisms. Then, the fundamental theories such as the Sabatier principle and scaling relations are reviewed, which provide a theoretical basis for catalytic activity. We also discuss thermodynamic, electronic, and geometric descriptors used to guide catalyst design and provide an in-depth discussion of their application and limitation. The advanced computational approaches, including high-throughput screening, machine learning, solvation model and Ab initio molecular dynamics, are also highlighted for the ability to accelerate catalyst discovery and simulate realistic reaction conditions. Finally, we propose future research directions aimed at searching universal descriptors, expanding data set, and integrating developing interpretable models with catalyst design.