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.

中文翻译：

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锂固体电解质体积和晶界电导率的基本结构和实验描述符

摘要 我们提出了一种计算方法，用于识别锂固体电解质离子电导率的重要描述符。我们的方法区分了很少报道的体积和晶界电导率的因素。通过机器研究相互关联的结构（例如晶粒尺寸、相）、材料（例如锂比率）、化学（例如电负性、极化率）和实验（例如烧结温度、合成方法）特性对体和晶界电导率的影响学习。使用室温下锂固体导体的体积和晶界电导率训练数据。重要的描述符由其特征重要性和预测性能阐明，由非线性 XGBoost 算法确定：