Yen-Ju Wu
(National Institute for Materials Science
)
;
Takehiro Tanaka
;
Tomoyuki Komori
;
Mikiya Fujii
;
Hiroshi Mizuno
;
Satoshi Itoh
(National Institute for Materials Science)
;
Tadanobu Takada
;
Erina Fujita
(National Institute for Materials Science)
;
Yibin Xu
(National Institute for Materials Science)
Collection
Citation
Description:
(abstract)We present a computational approach for identifying the important descriptors of the ionic conductivities of lithium solid electrolytes. Our approach discriminates the factors of both bulk and grain boundary conductivities, which have been rarely reported. The effects of the interrelated structural (e.g. grain size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability) and experimental (e.g. sintering temperature, synthesis method) properties on the bulk and grain boundary conductivities are investigated via machine learning. The data are trained using the bulk and grain boundary conductivities of Li solid conductors at room temperature. The important descriptors are elucidated by their feature importance and predictive performances, as determined by a nonlinear XGBoost algorithm: (i) the experimental descriptors of sintering conditions are significant for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell volume are the prior structural descriptors while the polarizability and electronegativity are the prior chemical descriptors for grain boundary, (iv) the grain size provides physical insights such as the thermodynamic condition and should be considered for determining grain boundary conductance in solid polycrystalline ionic conductors.
Rights:
Keyword: Ionic conductivity, machine learning, grain boundary, ionic conductor, Li battery, grain size, descriptor
Date published: 2020-01-31
Publisher: Informa UK Limited
Journal:
- SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (ISSN: 14686996) vol. 21 issue. 1 p. 712-725
Funding:
- Japan Science and Technology Agency ‘Materials research by Information Integration’ Initiative (MI2I) project (Panasonic-NIMS Center of Excellence for Advanced Functional Materials and ‘Materials research by Information Integration’ Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST))
Manuscript type: Publisher's version (Version of record)
MDR DOI:
First published URL: https://doi.org/10.1080/14686996.2020.1824985
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Updated at: 2024-01-05 22:11:22 +0900
Published on MDR: 2023-03-20 16:04:03 +0900