Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction
Luoyang Normal University · Nanjing University of Posts and Telecommunications · +8 more institutions
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,…
Citation impact
- FWCI
- 11.11
- Percentile
- 100%
- References
- 194
Authors
10Topics & keywords
- Electrocatalyst
- Nanoclusters
- Computer science
- Nanotechnology
- Machine learning
- Artificial intelligence
- Materials science
- Chemistry