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

Engineering and Technology

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

K. Li, J. Wang, et al.

Discover the innovative machine learning workflow that merges quantum calculations with graph convolutional neural networks to identify ionic liquids ideal for ionic polymer electrolytes in lithium metal batteries. This pioneering research, conducted by Kai Li, Jifeng Wang, Yuanyuan Song, and Ying Wang, results in IPE membranes boasting remarkable performance metrics.

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Playback language: English
Abstract
This paper presents a machine learning workflow combining quantum calculations and graph convolutional neural networks to discover ionic liquids (ILs) suitable for ionic polymer electrolytes (IPEs) in lithium metal batteries (LMBs). The workflow screens a large pool of IL candidates based on ionic conductivity and electrochemical window, leading to the experimental investigation of selected ILs in IPE membranes. These membranes demonstrate high critical current density, excellent capacity retention over numerous cycles, fast charge/discharge capabilities, and high efficiency, exceeding the performance of many other single-layer polymer electrolytes.
Publisher
Nature Communications
Published On
May 15, 2023
Authors
Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang
Tags
machine learning
ionic liquids
ionic polymer electrolytes
lithium metal batteries
electrochemical window
ionic conductivity
graph convolutional neural networks

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