Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes

Science and Technology of Advanced Materials
Yen-Ju WuYibin Xu

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 in...Continue Reading

References

Aug 2, 2011·Nature Materials·Noriaki KamayaAkio Mitsui
Jul 26, 2017·Journal of the American Chemical Society·Marvin A KraftWolfgang G Zeier
Nov 22, 2019·Nature Communications·Ying ZhangChen Ling

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Methods Mentioned

BETA
scanning
transmission electron microscopy
MDS

Software Mentioned

Scikit
MDS
XGBoost
learn
LASSO
NASICON

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