Next Article in Journal
Coenzyme Q10 as an Inhibitor of Effector Release from One-Electron-Reduced Bioreductive Anticancer Prodrugs
Previous Article in Journal
Structural Characterization and Anti-Gouty Nephropathy Potential of Polysaccharides from Atractylodes chinensis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 30
Issue 4
10.3390/molecules30040759
molecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction

by
Guohao Yin
1,
Haiyan Zhu
1,2,*,
Shanlin Chen
1,2,
Tingting Li
1,2,
Chou Wu
1,
Shaobo Jia
1,
Jianxiao Shang
1,
Zhequn Ren
1,
Tianhao Ding
1 and
Yawei Li
3,*
1
Shaanxi Key Laboratory for Theoretical Physics Frontiers, Institute of Modern Physics, Northwest University, Xi’an 710069, China
2
Institute of Yulin Carbon Neutral College, Northwest University, Xi’an 719000, China
3
School of Energy, Power and Mechanical Engineering, Institute of Energy and Power Innovation, North China Electric Power University, Beijing 102206, China
*
Authors to whom correspondence should be addressed.
Molecules 2025, 30(4), 759; https://doi.org/10.3390/molecules30040759
Submission received: 27 December 2024 / Revised: 2 February 2025 / Accepted: 2 February 2025 / Published: 7 February 2025
Browse Figures
Versions Notes

Abstract

Hydrogen as an environmentally friendly energy carrier, has many significant advantages, such as cleanliness, recyclability, and high calorific value of combustion, which makes it one of the major potential sources of energy supply in the future. Hydrogen evolution reaction (HER) is an important strategy to cope with the global energy shortage and environmental degradation, and given the large cost involved in HER, it is crucial to screen and develop stable and efficient catalysts. Compared with the traditional catalyst development model, the rapid development of data science and technology, especially machine learning technology, has shown great potential in the field of catalyst development in recent years. Among them, the research method of combining high-throughput computing and machine learning has received extensive attention in the field of materials science. Therefore, this paper provides a review of the recent research on combining high-throughput computing with machine learning to guide the development of HER electrocatalysts, covering the application of machine learning in constructing prediction models and extracting key features of catalytic activity. The future challenges and development directions of this field are also prospected, aiming to provide useful references and lessons for related research.
Keywords: hydrogen evolution reaction; machine learning; high throughput screening; density functional theory hydrogen evolution reaction; machine learning; high throughput screening; density functional theory
Graphical Abstract

Share and Cite

MDPI and ACS Style

Yin, G.; Zhu, H.; Chen, S.; Li, T.; Wu, C.; Jia, S.; Shang, J.; Ren, Z.; Ding, T.; Li, Y. Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction. Molecules 2025, 30, 759. https://doi.org/10.3390/molecules30040759

AMA Style

Yin G, Zhu H, Chen S, Li T, Wu C, Jia S, Shang J, Ren Z, Ding T, Li Y. Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction. Molecules. 2025; 30(4):759. https://doi.org/10.3390/molecules30040759

Chicago/Turabian Style

Yin, Guohao, Haiyan Zhu, Shanlin Chen, Tingting Li, Chou Wu, Shaobo Jia, Jianxiao Shang, Zhequn Ren, Tianhao Ding, and Yawei Li. 2025. "Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction" Molecules 30, no. 4: 759. https://doi.org/10.3390/molecules30040759

APA Style

Yin, G., Zhu, H., Chen, S., Li, T., Wu, C., Jia, S., Shang, J., Ren, Z., Ding, T., & Li, Y. (2025). Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction. Molecules, 30(4), 759. https://doi.org/10.3390/molecules30040759

Article Metrics

Back to TopTop