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Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

Authors :
Natalia Kireeva
Aslan Yu. Tsivadze
Vladislav S. Pervov
Source :
Batteries, Vol 9, Iss 9, p 430 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

All-solid-state batteries (ASSBs) are the important attributes of the forthcoming technologies for electrochemical energy storage. A key element of ASSBs is the solid electrolyte materials. Garnets are considered promising candidates for solid electrolytes of ASSBs due to their chemical stability with Li metal anodes, reasonable kinetic characteristics (σLi∼ 10−3–10−4 S · cm−1) and a wide electrochemical window. This study is aimed at the analysis of the experimental data available for garnet thin films, examining the ionic conductivity through the film/substrate lattice mismatch, the elastic properties and the difference in the thermal expansion characteristics of the film and the substrate, the deposition temperature of the film, and the melting point and the dielectric constant of the substrate. Based on the results of this analysis and by introducing the corresponding characteristics involved as the descriptors, the quantitative models for predicting the ionic conductivity values were developed. Some important characteristic features for ion transport in garnet films, which are primarily concerned with the film/substrate misfit, elastic properties, deposition temperature, cation segregation and the space charge effects, are discussed.

Details

Language :
English
ISSN :
23130105
Volume :
9
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Batteries
Publication Type :
Academic Journal
Accession number :
edsdoj.7f1dfec6e90408083f3b1aed6c349e2
Document Type :
article
Full Text :
https://doi.org/10.3390/batteries9090430