Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Wang, Guanjie; Wang, Changrui; Zhang, Xuanguang; Li, Zefeng; Zhou, Jian; Sun, Zhimei

doi:10.1016/j.isci.2024.109673

Cited by 6 publications

References 232 publications

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

Wang,

Zhou,

Luo

et al. 2024

Adv Funct Materials

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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.

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