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Artificial intelligence inferred microstructural properties from voltage–capacity curves

The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-base...

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Autores principales: Sun, Yixuan, Ayalasomayajula, Surya Mitra, Deva, Abhas, Lin, Guang, García, R. Edwin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352700/
https://www.ncbi.nlm.nih.gov/pubmed/35927411
http://dx.doi.org/10.1038/s41598-022-16942-5
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author Sun, Yixuan
Ayalasomayajula, Surya Mitra
Deva, Abhas
Lin, Guang
García, R. Edwin
author_facet Sun, Yixuan
Ayalasomayajula, Surya Mitra
Deva, Abhas
Lin, Guang
García, R. Edwin
author_sort Sun, Yixuan
collection PubMed
description The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn[Formula: see text] O[Formula: see text] chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 [Formula: see text] score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.
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spelling pubmed-93527002022-08-06 Artificial intelligence inferred microstructural properties from voltage–capacity curves Sun, Yixuan Ayalasomayajula, Surya Mitra Deva, Abhas Lin, Guang García, R. Edwin Sci Rep Article The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn[Formula: see text] O[Formula: see text] chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 [Formula: see text] score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries. Nature Publishing Group UK 2022-08-04 /pmc/articles/PMC9352700/ /pubmed/35927411 http://dx.doi.org/10.1038/s41598-022-16942-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sun, Yixuan
Ayalasomayajula, Surya Mitra
Deva, Abhas
Lin, Guang
García, R. Edwin
Artificial intelligence inferred microstructural properties from voltage–capacity curves
title Artificial intelligence inferred microstructural properties from voltage–capacity curves
title_full Artificial intelligence inferred microstructural properties from voltage–capacity curves
title_fullStr Artificial intelligence inferred microstructural properties from voltage–capacity curves
title_full_unstemmed Artificial intelligence inferred microstructural properties from voltage–capacity curves
title_short Artificial intelligence inferred microstructural properties from voltage–capacity curves
title_sort artificial intelligence inferred microstructural properties from voltage–capacity curves
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352700/
https://www.ncbi.nlm.nih.gov/pubmed/35927411
http://dx.doi.org/10.1038/s41598-022-16942-5
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