Ding Rui, Chen Junhong, Chen Yuxin, Liu Jianguo, Bando Yoshio, Wang Xuebin
Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL 60439, USA.
Chem Soc Rev. 2024 Nov 25;53(23):11390-11461. doi: 10.1039/d4cs00844h.
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.
机器学习（ML）正在迅速成为氢能产业中用于电催化剂的创制与优化的关键工具，这些电催化剂可增强诸如析氢反应（HER）、析氧反应（OER）、氢氧化反应（HOR）和氧还原反应（ORR）等关键电化学反应。这篇综述展示了前沿的机器学习技术如何在电催化剂设计中得以应用，以克服传统方法耗时的局限性。机器学习方法利用高通量实验的实验数据以及诸如密度泛函理论（DFT）模拟的计算数据，能够轻松识别电催化剂性能与关键材料描述符之间的复杂关联。凭借其无与伦比的速度和准确性，机器学习通过其模式识别能力推动了新型候选材料的发现以及已知产品的改进。本综述旨在以材料科学家易于理解的形式，对机器学习的应用进行详细分类阐述。因此，我们根据不同电化学反应中常见的研究材料类型，全面梳理了受机器学习驱动的研究，以说明机器学习如何巧妙地应对这些情况下描述符的复杂情况。我们进一步强调了机器学习在未来氢能转化电催化剂的发现与开发中的关键作用。还讨论了这一特定领域内有待填补的潜在挑战与差距。作为一份实用指南，我们希望这项工作能够弥合不同群体之间的差距，并鼓励电催化研究中的新范式，以实现更有效、更可持续的能源解决方案。
