Review of External Field Effects on Electrocatalysis: Machine Learning Guided Design

• Published in: Advanced functional materials 2024-09, Vol.34 (49), p.n/a
• Main Authors: Wang, Lei, Zhou, Xuyan, Luo, Zihan, Liu, Sida, Yue, Shengying, Chen, Yan, Liu, Yilun
• Format: Article
• Language: English
• Subjects: Chemical reactions; Chemical synthesis; data‐driven design; Design optimization; Electrocatalysis; Electrocatalysts; external field effect; Machine learning; machine‐learning interatomic potentials; strain effect
• ISSN: 1616-301X; 1616-3028
• DOI: 10.1002/adfm.202408870

External field‐enhanced electrocatalysis is a novel and promising approach for boosting the efficiency of electrocatalytic reactions, potentially achieving significant enhancement without altering the composition and structure of electrocatalysts. In addition, the scaling relations of electrocatalysis typically lead to similar variations of initial‐state and transition‐state (TS) energy, which minimally impacts the reaction energy barrier. A sophisticated design of the external field effects shall break these scaling relations. This review provides a comprehensive overview of current research on the effect of mechanical, electric, and magnetic fields on electrocatalysis. It meticulously details the mechanisms underlying activity enhancement based on external field regulations, spanning from the synthesis of electrocatalytic materials to their behavior during the reaction process and modulation of the electrolyte environment. Additionally, the applications of emerging machine learning (ML) technologies in electrocatalysis design, including machine learning interatomic potentials (MLIPs) to simulate large‐scale and dynamic chemical reaction processes, data‐driven design and optimization of electrocatalysis performance, are briefly reviewed. In addition, the significant potential of ML technologies in conjunction with external field regulation, envisioning them as effective tools for optimizing or reverse designing electrocatalysis, considering both thermodynamic and kinetic factors as well as the dynamic effect of electrocatalyst surfaces under extreme external fields, is highlighted. External field effects like mechanical, electric, and magnetic fields significantly impact electrocatalysis performance by enhancing adsorption, reducing reaction energy barriers, and breaking scaling laws. Traditional methods struggle to explore complex design space of electrocatalysis. Therefore, machine learning (ML) methods are crucial for optimizing electrocatalysis design under these external field effects.