Accelerating solid-state battery design: predicting ionic conductivity with machine learning potentials
Achieving high ionic conductivity in solid-state electrolytes (SSEs) is critical for next-generation batteries, yet exploring the vast chemical space is hindered by the computational expense of first-principles methods.
Authors
- P. Hu (ORCID: https://orcid.org/0000-0002-6318-1051)
- Ping Ji (ORCID: https://orcid.org/0000-0002-7841-6677)
- Xiaoyan Fu (ORCID: https://orcid.org/0009-0007-9084-9815)
- Zhongwei Zhang (ORCID: https://orcid.org/0000-0002-6447-5334)
- Jianhui Ma (ORCID: https://orcid.org/0000-0003-1000-2184)
- Ailian Wang
Institutions
- Queen's University Belfast (GB)
- ShanghaiTech University (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-12-06
- DOI
- https://doi.org/10.1039/d5ta07245j
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00