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)
コレクション
引用
説明:
(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.
権利情報:
キーワード: Ionic conductivity, machine learning, grain boundary, ionic conductor, Li battery, grain size, descriptor
刊行年月日: 2020-01-31
出版者: Informa UK Limited
掲載誌:
- SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (ISSN: 14686996) vol. 21 issue. 1 p. 712-725
研究助成金:
- 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))
原稿種別: 出版者版 (Version of record)
MDR DOI:
公開URL: https://doi.org/10.1080/14686996.2020.1824985
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更新時刻: 2024-01-05 22:11:22 +0900
MDRでの公開時刻: 2023-03-20 16:04:03 +0900