Integration of a self-attentive neural network and density functional theory for accelerated screening of graphene-based stabilized binary adsorption systems
It is crucial to study adsorbed atomic systems in graphene as exogenous atoms or molecules can precisely modulate the properties of graphene and significantly extend its wide-range applications.
Authors
- Longfei Guo (ORCID: https://orcid.org/0009-0002-9103-6107)
- Yong Liu (ORCID: https://orcid.org/0000-0001-6752-080X)
- Zongfan Wei
- Jingchuan Zhu (ORCID: https://orcid.org/0000-0002-6075-2373)
- Longlong Dong (ORCID: https://orcid.org/0000-0002-9197-5836)
- Jingteng Xue (ORCID: https://orcid.org/0009-0008-2809-3508)
- Jiaying Chen (ORCID: https://orcid.org/0000-0002-1295-574X)
Institutions
- Harbin Institute of Technology (CN)
- Northwest Institute For Non-Ferrous Metal Research (CN)
- Western Metal Materials (China) (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta01512j
- Primary Topic
- Graphene research and applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Shaanxi Provincial Science and Technology Department
- National Key Research and Development Program of China