Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning
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 wi...
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MDPI AG
2023-08-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/9/9/430 |
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| author | Natalia Kireeva Aslan Yu. Tsivadze Vladislav S. Pervov |
| author_facet | Natalia Kireeva Aslan Yu. Tsivadze Vladislav S. Pervov |
| author_sort | Natalia Kireeva |
| collection | DOAJ |
| description | 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>L</mi><mi>i</mi></mrow></msub></semantics></math></inline-formula>∼ 10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>–10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></semantics></math></inline-formula> S · cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) 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. |
| first_indexed | 2024-03-10T23:02:58Z |
| format | Article |
| id | doaj.art-d7f1dfec6e90408083f3b1aed6c349e2 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-10T23:02:58Z |
| publishDate | 2023-08-01 |
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| spelling | doaj.art-d7f1dfec6e90408083f3b1aed6c349e22023-11-19T09:33:19ZengMDPI AGBatteries2313-01052023-08-019943010.3390/batteries9090430Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine LearningNatalia Kireeva0Aslan Yu. Tsivadze1Vladislav S. Pervov2Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Leninsky Prosp, 31, Moscow 119071, RussiaFrumkin Institute of Physical Chemistry and Electrochemistry RAS, Leninsky Prosp, 31, Moscow 119071, RussiaKurnakov Institute of General and Inorganic Chemistry RAS, Leninsky Prosp, 31, Moscow 119071, RussiaAll-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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>L</mi><mi>i</mi></mrow></msub></semantics></math></inline-formula>∼ 10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>–10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></semantics></math></inline-formula> S · cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) 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.https://www.mdpi.com/2313-0105/9/9/430solid electrolytesthin filmsionic conductivityelastic characteristicsmachine learning |
| spellingShingle | Natalia Kireeva Aslan Yu. Tsivadze Vladislav S. Pervov Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning Batteries solid electrolytes thin films ionic conductivity elastic characteristics machine learning |
| title | Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning |
| title_full | Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning |
| title_fullStr | Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning |
| title_full_unstemmed | Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning |
| title_short | Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning |
| title_sort | predicting ionic conductivity in thin films of garnet electrolytes using machine learning |
| topic | solid electrolytes thin films ionic conductivity elastic characteristics machine learning |
| url | https://www.mdpi.com/2313-0105/9/9/430 |
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