Design and Application of Electrocatalyst Based on Machine Learning

IF 24.5 Q1 CHEMISTRY, PHYSICAL
Yulan Gu, Hailong Zhang, Zhen Xu, Rui Ren, Xiangyi Kong, Yafu Wang, Houen Zhu, Dongdong Xue, Yali Zhang, Yuzhu Ma, Dongyuan Zhao, Jiangwei Zhang
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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.

基于机器学习的电催化剂设计与应用
数据驱动的人工智能为解决全球能源和环境问题提供了强有力的技术支撑。机器学习(ML)强大的数据处理和分析能力可以快速预测电催化性能,提高催化剂设计的效率,解决传统催化剂设计耗时和低效的问题。通过将机器学习与理论计算和实验相结合,可以精确调节催化反应过程。这不仅加速了新催化剂的发现,而且还推动了更高效、更环保的可持续能源技术的发展。在本文中,我们讨论了发现由机器学习驱动的新型催化剂的新方法,重点是催化活性预测,反应能垒优化和创新催化材料的设计。系统分析了机器学习在电催化领域的应用,并对机器学习在电催化领域的应用前景进行了展望。我们对机器学习在电催化领域的应用进行了全面深入的分析,并探讨了其未来的发展潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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