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Abstract
Highlights
The process of machine learning is introduced in detail.
Recent developments in machine learning for low-dimensional electrocatalysts are briefly reviewed.
Future directions and perspectives for machine learning in hydrogen evolution reaction are critically discussed.
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.
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1 Luoyang Normal University, College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang, People’s Republic of China (GRID:grid.440830.b) (ISNI:0000 0004 1793 4563)
2 Nanjing University of Posts and Telecommunications (NUPT), New Energy Technology Engineering Lab of Jiangsu Province, College of Science, Nanjing, People’s Republic of China (GRID:grid.453246.2) (ISNI:0000 0004 0369 3615)
3 University of Science and Technology Beijing, School of Materials Science and Engineering, Beijing, People’s Republic of China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); University of Nebraska-Lincoln, Department of Chemistry, Lincoln, USA (GRID:grid.24434.35) (ISNI:0000 0004 1937 0060)
4 Henan University of Science and Technology, Henan Key Laboratory of High-Temperature Structural and Functional Materials, National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Luoyang, People’s Republic of China (GRID:grid.453074.1) (ISNI:0000 0000 9797 0900)
5 National Engineering Laboratory for Risk Perception and Prevention, Beijing, People’s Republic of China (GRID:grid.453074.1)
6 Shanghai Jiao Tong University, Center of Hydrogen Science, Shanghai, People’s Republic of China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293); Shanghai Jiao Tong University, State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai, People’s Republic of China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293)
7 Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Materials, Xiamen, People’s Republic of China (GRID:grid.12955.3a) (ISNI:0000 0001 2264 7233)