Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

Multi-atomic loaded C2N1 catalysts for CO2 reduction to CO or formic acid DOI
Yimeng Sun, Lin Tao, Mingjie Wu

et al.

Nanoscale, Journal Year: 2024, Volume and Issue: 16(20), P. 9791 - 9801

Published: Jan. 1, 2024

Triple-atom catalysts exhibit moderate adsorption energy for intermediate species, enabling the optimal performance of CO 2 electrocatalytic reduction reaction.

Language: Английский

Citations

19
Recent advances in the development of single atom catalysts for oxygen evolution reaction DOI Creative Commons
Sai Li,

Zeyi Xin,

Yue Luo

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 82, P. 1081 - 1100

Published: Aug. 9, 2024

Language: Английский

Citations

14
Fully automated high-throughput computer-based catalytic material screening framework and its application on the new-generation Tianhe supercomputer DOI
Can Leng, Xuguang Chen, Jie Liu

et al.

Computational Materials Science, Journal Year: 2025, Volume and Issue: 252, P. 113775 - 113775

Published: Feb. 23, 2025

Language: Английский

Citations

0
Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

Citations

0