Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

Abstract

Abstract Electrocatalytic hydrogen evolution reaction (HER) is a promising strategy to solve and mitigate the coming energy shortage and global environmental pollution. Searching for efficient electrocatalysts for HER remains challenging through traditional trial‐and‐error methods from numerous potential material candidates. Theoretical high throughput calculation assisted by machine learning is a possible method to screen excellent HER electrocatalysts effectively. This will pave the way for high‐efficiency and low‐price electrocatalyst findings. In this review, we comprehensively introduce the machine learning workflow and standard models for hydrogen reduction reactions. This mainly illustrates how machine learning is used in catalyst filtration and descriptor exploration. Subsequently, several applications, including surface electrocatalysts, two‐dimensional (2D) electrocatalysts, and single/dual atom electrocatalysts using machine learning in electrocatalytic HER, are highlighted and introduced. Finally, the corresponding challenge and perspective for machine learning in electrocatalytic hydrogen reduction reactions are concluded. We hope this critical review can provide a comprehensive understanding of machine learning for HER catalyst design and guide the future theoretical and experimental investigation of HER catalyst findings.

Keywords

• hydrogen evolution reaction
• machine learning
• density function theory
• descriptors