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

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Interpretable hybrid machine learning demystifies the degradation of practical lithium-sulfur batteries
Title:
Interpretable hybrid machine learning demystifies the degradation of practical lithium-sulfur batteries
Journal Title:
Journal of Energy Chemistry
Authors:
Publication Date:
11 January 2023
Citation:
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
Abstract:
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.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research is supported by core funding from: SERC
Grant Reference no. : NA
Description:
ISSN:
2095-4956
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