Interpretable hybrid machine learning demystifies the degradation of practical lithium-sulfur batteries

Interpretable hybrid machine learning demystifies the degradation of practical lithium-sulfur batteries

Journal of Energy Chemistry

10.1016/j.jechem.2022.12.003

http://dx.doi.org/10.1016/j.jechem.2022.12.003

Zhi Wei Seh

Electrochemistry, Energy Engineering and Power Technology, Fuel Technology, Energy (miscellaneous)

11 January 2023

Seh, Z. W. (2023). Interpretable hybrid machine learning demystifies the degradation of practical lithium-sulfur batteries. Journal of Energy Chemistry, 79, 54–55. https://doi.org/10.1016/j.jechem.2022.12.003

An interpretable hybrid machine learning framework was proposed to demystify the degradation of practical lithium–sulfur batteries by incorporating in-situ computations and model explicability for novel battery prognosis and development.

Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

This research is supported by core funding from: SERC
Grant Reference no. : NA

https://oar.a-star.edu.sg/communities-collections/articles/19137

2095-4956

Institute of Materials Research and Engineering