.png){kind=icon}
Return
Machine learning and data-driven methods in computational surface and interface science
.png){kind=icon}
OA
AI
DOI：10.1038/s41524-025-01691-6.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machine learning and data-driven methods have started to transform the study of surfaces and interfaces. Here, we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling grand challenges in computational surface science from 2D materials to interface engineering and electrocatalysis. Challenges remain, including the scarcity of large datasets and the need for more electronic structure methods for interfaces.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.9
Papers:
2.4K
Citations:
1.7W
Organization
Cited Papers
Machine learning the metastable phase diagram of covalently bonded carbon.png)
NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveE(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveSpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effectsNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveHot-electron effects during reactive scattering of H2 from Ag(111): the interplay between mode-specific electronic friction and the potential energy landscapeCHEMICAL SCIENCEOAAI
CHEMICAL SCIENCEIF7.4
OAAIShareSave
View more