Issue

Vol. 15 (2023)

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

https://doi.org/10.1007/s40820-023-01192-5
Jin Li
College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function‑Oriented Porous Materials, Luoyang Normal University, Luoyang 471934, People’s Republic of China
Naiteng Wu
College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function‑Oriented Porous Materials, Luoyang Normal University, Luoyang 471934, People’s Republic of China
Jian Zhang
New Energy Technology Engineering Lab of Jiangsu Province, College of Science, Nanjing University of Posts and Telecommunications (NUPT), Nanjing 210023, People’s Republic of China
Hong‑Hui Wu
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China
Kunming Pan
Henan Key Laboratory of High‑Temperature Structural and Functional Materials, National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan University of Science and Technology, Luoyang 471003, People’s Republic of China
Yingxue Wang
National Engineering Laboratory for Risk Perception and Prevention, Beijing 100041, People’s Republic of China
Guilong Liu
College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function‑Oriented Porous Materials, Luoyang Normal University, Luoyang 471934, People’s Republic of China
Xianming Liu
College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function‑Oriented Porous Materials, Luoyang Normal University, Luoyang 471934, People’s Republic of China
Zhenpeng Yao
Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai 200000, People’s Republic of China
Qiaobao Zhang
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Materials, Xiamen University, Xiamen 361005, People’s Republic of China

Corresponding Author: Qiaobao Zhang

zhangqiaobao@xmu.edu.cn

Nano-Micro Letters, Vol. 15 (2023), Article Number: 227

Abstract

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.


Highlights:
1 The process of machine learning is introduced in detail.
2 Recent developments in machine learning for low-dimensional electrocatalysts are briefly reviewed.
3 Future directions and perspectives for machine learning in hydrogen evolution reaction are critically discussed.

Keywords

Machine learning Hydrogen evolution reaction Low-dimensional electrocatalyst Descriptor Algorithm
Li, Jin, Naiteng Wu, Jian Zhang, Hong‑Hui Wu, Kunming Pan, Yingxue Wang, Guilong Liu, Xianming Liu, Zhenpeng Yao, and Qiaobao Zhang. 2023. “Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction”. Nano-Micro Letters 15 (October):227. https://doi.org/10.1007/s40820-023-01192-5.
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