Leveraging Machine Learning for Accelerated Electrode–Electrolyte Interface Design in Rechargeable Li‐Based Batteries

Xiaorui Liu, Qingyu Li, Jianghao Liang, Zhiqiang Li, Haozhi Wang, Yida Deng

Open source

DOI
10.1002/smll.74632
Published
2026-08-14
Container
Small
Publisher
Wiley
Open access
unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1002/smll.74632,
  title = {Leveraging Machine Learning for Accelerated Electrode–Electrolyte Interface Design in Rechargeable Li‐Based Batteries},
  author = {Xiaorui Liu and Qingyu Li and Jianghao Liang and Zhiqiang Li and Haozhi Wang and Yida Deng},
  year = {2026},
  journal = {Small},
  doi = {10.1002/smll.74632},
  url = {https://doi.org/10.1002/smll.74632}
}

RIS

TY  - JOUR
TI  - Leveraging Machine Learning for Accelerated Electrode–Electrolyte Interface Design in Rechargeable Li‐Based Batteries
AU  - Xiaorui Liu
AU  - Qingyu Li
AU  - Jianghao Liang
AU  - Zhiqiang Li
AU  - Haozhi Wang
AU  - Yida Deng
PY  - 2026
JO  - Small
DO  - 10.1002/smll.74632
UR  - https://doi.org/10.1002/smll.74632
ER  - 

APA

Liu, X., Li, Q., Liang, J., Li, Z., Wang, H., & Deng, Y. (2026). Leveraging Machine Learning for Accelerated Electrode–Electrolyte Interface Design in Rechargeable Li‐Based Batteries. Small. https://doi.org/10.1002/smll.74632

Source records