Unsupervised machine learning accelerates solid electrolyte discovery

被引：1
|
作者：
Xu Zhang [1 ]
Bin Tang [1 ]
Zhen Zhou [1 ]
机构：
[1] School of Materials Science and Engineering, Computational Centre for Molecular Science, Institute of New Energy Material Chemistry, Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), Renewable Energy Conversion and Storage Cent
来源：
Green Energy & Environment | 2021年 / 6卷 / 01期
关键词：
D O I：
暂无
中图分类号：
TM912 [蓄电池];
学科分类号：
0808 ;
摘要：
Traditional organic liquid electrolytes used in commercial Li-ion batteries would incur serious safety issues due to their flammability and volatility [1]. The exploration and design of solid electrolytes with high room-temperature Li-ion conductivities (σ;) are important to improve the safety and cycle life of Li-ion batteries [2]. Although previous investigations have proven that various physical factors correlate with Li-ion diffusion in solids,
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页码：3 / 4
页数：2
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