Data-driven design of advanced magnesium-battery electrolyte <i>via</i> dynamic solvation models
Artificial intelligence (AI) facilitates electrolyte screening by correlating the complex physicochemical properties of solvent/clusters with battery performance.
Authors
- Zhengqing Fan
- Zhijun Zuo (ORCID: https://orcid.org/0000-0003-2185-4817)
- Rushuai Li
- Alan Meng (ORCID: https://orcid.org/0000-0002-4733-760X)
- Wanyu Zhao (ORCID: https://orcid.org/0000-0002-5615-3589)
- Jiayi Li (ORCID: https://orcid.org/0000-0002-7756-7203)
- Ruimin Li (ORCID: https://orcid.org/0000-0002-9635-780X)
- Xiaowei Yang (ORCID: https://orcid.org/0000-0002-4862-7422)
Institutions
- Shanghai Jiao Tong University (CN)
- Taiyuan University of Technology (CN)
Publication Details
- Journal
- Energy & Environmental Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ee01304f
- Citations
- 14
- Primary Topic
- Advanced Battery Materials and Technologies
- Type
- article
- Field-Weighted Citation Impact
- 10.23
Funders
- National Natural Science Foundation of China
- ShanXi Science and Technology Department