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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9352700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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