Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes
| Autor: | Hiroshi Mizuno, Yen Ju Wu, Erina Fujita, Yibin Xu, Takehiro Tanaka, Mikiya Fujii, Tadanobu Takada, Tomoyuki Komori, Satoshi Itoh |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Materials science
404 Materials informatics / Genomics li battery Thermodynamics Sintering 02 engineering and technology 010402 general chemistry 01 natural sciences Energy Materials Electronegativity Phase (matter) Fast ion conductor Ionic conductivity descriptor General Materials Science Materials of engineering and construction. Mechanics of materials grain size 206 Energy conversion / transport / storage / recovery 021001 nanoscience & nanotechnology ionic conductor Grain size 0104 chemical sciences machine learning grain boundary ionic conductivity TA401-492 Grain boundary Crystallite 0210 nano-technology 107 Glass and ceramic materials TP248.13-248.65 Research Article Biotechnology |
| Zdroj: | Science and Technology of Advanced Materials, Vol 21, Iss 1, Pp 712-725 (2020) Science and Technology of Advanced Materials article-version (VoR) Version of Record |
| ISSN: | 1878-5514 1468-6996 |
| Popis: | 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. GRAPHICAL ABSTRACT |
| Databáze: | OpenAIRE |
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