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Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries

As essential components of ionic polymer electrolytes (IPEs), ionic liquids (ILs) with high ionic conductivity and wide electrochemical window are promising candidates to enable safe and high-energy-density lithium metal batteries (LMBs). Here, we describe a machine learning workflow embedded with q...

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Autores principales: Li, Kai, Wang, Jifeng, Song, Yuanyuan, Wang, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10185508/
https://www.ncbi.nlm.nih.gov/pubmed/37188717
http://dx.doi.org/10.1038/s41467-023-38493-7
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author Li, Kai
Wang, Jifeng
Song, Yuanyuan
Wang, Ying
author_facet Li, Kai
Wang, Jifeng
Song, Yuanyuan
Wang, Ying
author_sort Li, Kai
collection PubMed
description As essential components of ionic polymer electrolytes (IPEs), ionic liquids (ILs) with high ionic conductivity and wide electrochemical window are promising candidates to enable safe and high-energy-density lithium metal batteries (LMBs). Here, we describe a machine learning workflow embedded with quantum calculation and graph convolutional neural network to discover potential ILs for IPEs. By selecting subsets of the recommended ILs, combining with a rigid-rod polyelectrolyte and a lithium salt, we develop a series of thin (~50 μm) and robust (>200 MPa) IPE membranes. The Li|IPEs|Li cells exhibit ultrahigh critical-current-density (6 mA cm(−2)) at 80 °C. The Li|IPEs|LiFePO(4) (10.3 mg cm(−2)) cells deliver outstanding capacity retention in 350 cycles (>96% at 0.5C; >80% at 2C), fast charge/discharge capability (146 mAh g(−1) at 3C) and excellent efficiency (>99.92%). This performance is rarely reported by other single-layer polymer electrolytes without any flammable organics for LMBs.
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spelling pubmed-101855082023-05-17 Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries Li, Kai Wang, Jifeng Song, Yuanyuan Wang, Ying Nat Commun Article As essential components of ionic polymer electrolytes (IPEs), ionic liquids (ILs) with high ionic conductivity and wide electrochemical window are promising candidates to enable safe and high-energy-density lithium metal batteries (LMBs). Here, we describe a machine learning workflow embedded with quantum calculation and graph convolutional neural network to discover potential ILs for IPEs. By selecting subsets of the recommended ILs, combining with a rigid-rod polyelectrolyte and a lithium salt, we develop a series of thin (~50 μm) and robust (>200 MPa) IPE membranes. The Li|IPEs|Li cells exhibit ultrahigh critical-current-density (6 mA cm(−2)) at 80 °C. The Li|IPEs|LiFePO(4) (10.3 mg cm(−2)) cells deliver outstanding capacity retention in 350 cycles (>96% at 0.5C; >80% at 2C), fast charge/discharge capability (146 mAh g(−1) at 3C) and excellent efficiency (>99.92%). This performance is rarely reported by other single-layer polymer electrolytes without any flammable organics for LMBs. Nature Publishing Group UK 2023-05-15 /pmc/articles/PMC10185508/ /pubmed/37188717 http://dx.doi.org/10.1038/s41467-023-38493-7 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Kai
Wang, Jifeng
Song, Yuanyuan
Wang, Ying
Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
title Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
title_full Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
title_fullStr Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
title_full_unstemmed Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
title_short Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
title_sort machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10185508/
https://www.ncbi.nlm.nih.gov/pubmed/37188717
http://dx.doi.org/10.1038/s41467-023-38493-7
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