Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
RMIT University · University of Nottingham · +2 more institutions
Abstract
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research…
Citation impact
- FWCI
- 31.12
- Percentile
- 100%
- References
- 398
Authors
5Topics & keywords
- Photocatalysis
- Electrocatalyst
- Nanotechnology
- Chemistry
- Biochemical engineering
- Computer science
- Catalysis
- Materials science