Machine learning-assisted design of oxygen-containing inorganic coating materials on a separator for lithium metal anodes
A high-throughput screening workflow integrated with machine learning and calculations has been used to identify possible materials. Both characterization and electrochemical test results show that HfO 2 @PP exhibits the best performance.
Authors
- Tianyang Yu
- Feng Wu (ORCID: https://orcid.org/0000-0003-0824-9508)
- Teng Zhao (ORCID: https://orcid.org/0000-0002-2398-2495)
- Ji Qian
- Renjie Chen (ORCID: https://orcid.org/0000-0002-3783-5410)
- Chenxi Xu (ORCID: https://orcid.org/0009-0000-4632-8392)
- Wangming Tang
- Ke Wang
- Li Li
Institutions
- Beijing Institute of Technology (CN)
- Key Laboratory of Nuclear Radiation and Nuclear Energy Technology (CN)
- South China Institute of Collaborative Innovation (CN)
- Zhuhai Institute of Advanced Technology (CN)
Publication Details
- Journal
- Inorganic Chemistry Frontiers
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5qi00111k
- Citations
- 1
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 1.24