Early prediction of lithium-ion battery degradation with a generative pre-trained transformer
- DOI
- 10.1038/s41467-025-66819-0
- Published
- 2025-12-05
- Container
- Nature Communications
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41467-025-66819-0,
title = {Early prediction of lithium-ion battery degradation with a generative pre-trained transformer},
author = {Jincheng Hu and Pengyu Fu and Zhongbao Wei and Yanjun Huang and Juliana Early and Ashley Fly and Yuanjian Zhang},
year = {2025},
journal = {Nature Communications},
doi = {10.1038/s41467-025-66819-0},
url = {https://doi.org/10.1038/s41467-025-66819-0}
}RIS
TY - JOUR TI - Early prediction of lithium-ion battery degradation with a generative pre-trained transformer AU - Jincheng Hu AU - Pengyu Fu AU - Zhongbao Wei AU - Yanjun Huang AU - Juliana Early AU - Ashley Fly AU - Yuanjian Zhang PY - 2025 JO - Nature Communications DO - 10.1038/s41467-025-66819-0 UR - https://doi.org/10.1038/s41467-025-66819-0 ER -
APA
Hu, J., Fu, P., Wei, Z., Huang, Y., Early, J., Fly, A., & Zhang, Y. (2025). Early prediction of lithium-ion battery degradation with a generative pre-trained transformer. Nature Communications. https://doi.org/10.1038/s41467-025-66819-0
Source records
- crossref · retrieved 2026-09-27T01:35:05.844Z