Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion

Electrochemical reactions are pivotal for energy conversion and storage to achieve a carbon-neutral and sustainable society, and optimal electrocatalysts are essential for their industrial applications. Theoretical modeling methodologies, such as density functional theory (DFT) and molecular dynamics (MD), efficiently assess electrochemical reaction mechanisms and electrocatalyst performance at atomic and molecular levels. However, its intrinsic algorithm limitations and high computational costs for large-scale systems generate gaps between experimental observations and calculation simulation, restricting the accuracy and efficiency of electrocatalyst design. Combining machine learning (ML) is a promising strategy to accelerate the development of electrocatalysts. The ML-DFT frameworks establish accurate property–structure–performance relations to predict and verify novel electrocatalysts' properties and performance, providing a deep understanding of reaction mechanisms. The ML-based methods also accelerate the solution of MD and DFT. Moreover, integrating ML and experiment characterization techniques represents a cutting-edge approach to providing insights into the structural, electronic, and chemical changes under working conditions. This review will summarize the DFT development and the current ML application status for electrocatalyst design in various electrochemical energy conversions. The underlying physical fundaments, application advancements, and challenges will be summarized. Finally, future research directions and prospects will be proposed to guide novel electrocatalyst design for the sustainable energy revolution.