Machine learning-guided substrate temperature optimization for anisotropic Fe(Se, Te) superconducting thin films
This paper presents the fabrication of Fe(Se, Te) superconducting films via machine-learning-assisted processes. These findings provide novel insights into the optimization of Fe(Se, Te) superconducting films.
Authors
- Jixing Liu (ORCID: https://orcid.org/0000-0003-4205-3744)
- Fanggong Cai (ORCID: https://orcid.org/0000-0002-5791-3287)
- Yong Zhao (ORCID: https://orcid.org/0000-0002-8774-3629)
- Yalin Zhang (ORCID: https://orcid.org/0009-0002-0476-3323)
- Shengnan Zhang (ORCID: https://orcid.org/0000-0003-3284-7954)
- Jie Zhang (ORCID: https://orcid.org/0009-0000-9532-8655)
- Yan Guo (ORCID: https://orcid.org/0000-0001-5449-7970)
- Yi Luo (ORCID: https://orcid.org/0000-0002-1528-788X)
- Chuanyu Zhang
- Yu Feng
Institutions
- Xihua University (CN)
- Fujian Normal University (CN)
- Chengdu University of Technology (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Key Laboratory of Guangdong Province (CN)
- Northwest Institute For Non-Ferrous Metal Research (CN)
- Southwest Jiaotong University (CN)
Publication Details
- Journal
- Journal of Materials Chemistry C
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5tc00059a
- Citations
- 1
- Primary Topic
- Iron-based superconductors research
- Type
- article
- Field-Weighted Citation Impact
- 0.43
Funders
- Ministry of Education