Rational electrolyte design for Li-metal batteries operated under extreme conditions: a combined DFT, COSMO-RS, and machine learning study

Wu, Liang-Ting; Zhan, Yu-Ting; Li, Zhong-Lun; Chen, Po-Ting; Hwang, Bing Joe; Jiang, Jyh-Chiang

doi:10.1039/d4ta03026e

J. Mater. Chem. A

2024

DOI: 10.1039/d4ta03026e

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Rational electrolyte design for Li-metal batteries operated under extreme conditions: a combined DFT, COSMO-RS, and machine learning study

Liang-Ting Wu,

Yu-Ting Zhan,

Zhong-Lun Li

et al.

Abstract: Developing electrolytes for Li metal batteries capable of operating under extreme conditions is a significant challenge and is often hindered by the absence of systematic solvent screening studies. In this...

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Design and Optimization of Multicomponent Electrolytes for Lithium‐Sulfur Battery: A Machine Learning Concept and Outlook

Qiu,

Zuo,

et al. 2024

ChemCatChem

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Lithium‐sulfur batteries (LSBs) have attracted increasing attention in the past decades due to their great potential to the next‐generation high‐energy‐density storage systems. As important as electrodes, electrolytes that could strongly determine battery performance via component regulation have left big difficulties in clarifying their complex interactions caused by multicomponent as well as the intricate formation mechanism of passivation layers at the electrolyte‐electrode interfaces. Fortunately, machine learning (ML), which is time‐saving and highly efficient, has played an irreplaceable role in accelerating discovery, design, and optimization of novel electrochemical energy storage materials. In this concept, we summarize the complex issues present in multicomponent electrolytes and focus on optimization of electrolyte formulations based on ML in order to provide an outlook on the recent development and perspectives on LSBs from the viewpoint of high‐performance electrolytes.

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Design and Optimization of Multicomponent Electrolytes for Lithium‐Sulfur Battery: A Machine Learning Concept and Outlook

Qiu,

Zuo,

et al. 2024

ChemCatChem

View full text Add to dashboard Cite

show abstract

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Rational electrolyte design for Li-metal batteries operated under extreme conditions: a combined DFT, COSMO-RS, and machine learning study

Abstract: Developing electrolytes for Li metal batteries capable of operating under extreme conditions is a significant challenge and is often hindered by the absence of systematic solvent screening studies. In this...

Cited by 1 publication

References 90 publications

Design and Optimization of Multicomponent Electrolytes for Lithium‐Sulfur Battery: A Machine Learning Concept and Outlook

Design and Optimization of Multicomponent Electrolytes for Lithium‐Sulfur Battery: A Machine Learning Concept and Outlook

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