Targeted design of advanced electrocatalysts by machine learning

Abstract

Exploring the production and application of clean energy has always been the core of sustainable development. As a clean and sustainable technology, electrocatalysis has been receiving widespread attention. It is crucial to achieve efficient, stable and cheap electrocatalysts. However, the traditional “trial and error” method is time-consuming, laborious and costly. In recent years, with the significant increase in computing power, computations have played an important role in electrocatalyst design. Nevertheless, it is still difficult to search for advanced electrocatalysts in the vast chemical space through traditional density functional theory (DFT) computations. Fortunately, the development of machine learning and interdisciplinary integration will inject new impetus into targeted design of electrocatalysts. Machine learning is able to predict electrochemical performances with an accuracy close to DFT. Here we provide an overview of the application of machine learning in electrocatalyst design, including the prediction of structure, thermodynamic properties and kinetic barriers. We also discuss the potential of explicit solvent model combined with machine learning molecular dynamics in this field. Finally, the favorable circumstances and challenges are outlined for the future development of machine learning in electrocatalysis. The studies on electrochemical processes by machine learning will further realize targeted design of high-efficiency electrocatalysts.