Citation: Yang LU, Xiang CHEN, Chen-Zi ZHAO, Qiang ZHANG. Machine Learning towards Screening Solid-state Lithium Ion Conductors[J]. Chinese Journal of Structural Chemistry, ;2020, 39(1): 8-10. doi: 10.14102/j.cnki.0254-5861.2011-2710 shu

Machine Learning towards Screening Solid-state Lithium Ion Conductors

Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China

Corresponding author: Qiang ZHANG, zhang-qiang@mails.tsinghua.edu.cn
Received Date: 20 December 2019
Accepted Date: 26 December 2019

Fund Project: the National Key Research and Development Program 2016YFA0202500National Natural Science Foundation of China 21825501

Figures(1)

Machine learning is an emerging method to discover new materials with specific characteristics. An unsupervised machine learning research is highlighted to discover new potential lithium ionic conductors by screening and clustering lithium compounds, providing inspirations for the development of solid-state electrolytes and practical batteries.
- unsupervised machine learning,
- first principles calculation,
- solid state lithium ion conductors,
- anion frameworks,
- high ionic conductivities
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