Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window
We construct an electrochemical window (ECW) dataset of over 16 000 Li-containing compounds using a thermodynamic approach for solid-state electrolytes (SSEs).
Authors
- Yaoshu Xie (ORCID: https://orcid.org/0000-0002-0725-0091)
- Zihui Li
- Jiajing Chen (ORCID: https://orcid.org/0000-0003-0523-4132)
- Yanbing He
- Rongni Li (ORCID: https://orcid.org/0000-0002-5631-6501)
- Shendong Tan
- Tingzheng Hou (ORCID: https://orcid.org/0000-0002-7163-2561)
- Ming Liu (ORCID: https://orcid.org/0000-0003-0569-7073)
- Xiang Zhang (ORCID: https://orcid.org/0000-0002-8014-7859)
- Jun Yang (ORCID: https://orcid.org/0000-0002-5270-3803)
- Chen Bai (ORCID: https://orcid.org/0009-0001-2516-1367)
- Lu Jiang (ORCID: https://orcid.org/0000-0002-8611-3828)
Institutions
- City University of Hong Kong (HK)
- Dongguan University of Technology (CN)
- Tsinghua–Berkeley Shenzhen Institute (CN)
- City College of Dongguan University of Technology (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta04197j
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.59