An interpretable machine learning model for Mn-based cathode development: mapping synthesis parameters to cycling stability
An efficient and transparent machine learning model is proposed and verified for predicting manganese-based cathode materials with high cycling stability.
Authors
- Haokun Li
- Zhimin Li (ORCID: https://orcid.org/0000-0002-3576-6175)
- Jingyuan Guo
- Z. Cui (ORCID: https://orcid.org/0000-0002-4317-2449)
- Yihang Li (ORCID: https://orcid.org/0000-0001-5677-0440)
- Cheng Wei (ORCID: https://orcid.org/0009-0004-0352-961X)
- Hongyu Yang (ORCID: https://orcid.org/0000-0001-5778-5396)
- Yue Hao (ORCID: https://orcid.org/0000-0002-8081-2919)
Institutions
- Xidian University (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta04758g
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52