Citation: Na Qin, Wenxin Guo, Fangxiu Li, Houfeng Zhang, Hong Liu, Chang Zhang, Lipiao Bao, Lei Liu, Muneerah Alomar, Siqi Zhao, Jian Zhang, Xing Lu. Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction[J]. Chinese Chemical Letters, ;2026, 37(3): 112021. doi: 10.1016/j.cclet.2025.112021 shu

Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction

Na Qin ^{a, 1} ,
Wenxin Guo ^{b, 1} ,
Fangxiu Li ^a ,
Houfeng Zhang ^a ,
Hong Liu ^a ,
Chang Zhang ^{a, c} ,
Lipiao Bao ^{a, c} ,
Lei Liu ^d ,
Muneerah Alomar ^e ,
Siqi Zhao ^{a, *,} ,
Jian Zhang ^{a, *,} ,
Xing Lu ^{a, c, *,}

a.
School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China
b.
Key Laboratory of Precision and Intelligent/School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
c.
School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
d.
School of HUST-Wuxi Research Institute, Wuxi 214174, China
e.
Department of Physics, College of Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia

sqzhao@hainanu.edu.cn

zhangjian7@hust.edu.cn

lux@hust.edu.cn

Received Date: 24 June 2025
Revised Date: 20 October 2025
Accepted Date: 22 October 2025
Available Online: 24 October 2025

Figures(9)

The hydrogen evolution reaction (HER) is a pivotal process for clean energy conversion, yet the development of efficient and cost-effective electrocatalysts remains a major challenge. Alloy catalysts, with their tunable electronic properties and promising catalytic performance, have shown great potential for HER. However, the design of component types and ratios, along with structural optimization, has largely relied on traditional trial-and-error approaches, which are very complex and time-consuming. The rise of machine learning (ML) provides an efficient strategy for discovering and optimizing alloy catalysts by enabling rapid analysis of extensive experimental and simulation datasets. This review highlights the recent advances in applying ML techniques for the design and optimization of alloy electrocatalysts for HER, covering binary and multinary (ternary, quaternary and high-entropy alloys). In particular, by employing supervised learning and deep learning techniques, ML has achieved remarkable success in the rapid screening of alloy catalysts and in improving prediction accuracy. It also demonstrates the merit and capability of ML in accelerating this process. In the end, we discuss current challenges and future prospects for integrating ML into advanced HER catalysis, highlighting its potential to revolutionize catalyst development and promote sustainable hydrogen energy solutions.
- Hydrogen evolution reaction,
- Machine learning,
- Alloy catalysts,
- Artificial intelligence,
- Deep learning
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沈阳化工大学材料科学与工程学院沈阳 110142