Design and Application of Electrocatalyst Based on Machine Learning

Abstract

Data-driven artificial intelligence provides strong technical support for addressing global energy and environmental issues. The powerful data processing and analysis capabilities of machine learning (ML) can quickly predict electrocatalytic performance, improving the efficiency of catalyst design and addressing the time-consuming and inefficient nature of traditional catalyst design. By integrating ML with theoretical calculations and experiments, catalytic reaction processes can be precisely regulated. This not only accelerates the discovery of new catalysts but also drives the development of more efficient and environmentally friendly sustainable energy technologies. In this article, we discuss new approaches to discovering novel catalysts driven by ML, focusing on catalytic activity prediction, reaction energy barrier optimization, and the design of innovative catalytic materials. We systematically analysis the application of ML in the field of electrocatalysis and explore the future prospects of ML in this domain. We provide a comprehensive and in-depth analysis of the application of ML in the field of electrocatalysis and explore its potential for future development.

Keywords

catalytic activity / density functional theory / electrocatalysis / machine learning

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