Optimization of heterogeneous ternary Li3PO4-Li3BO3-Li2SO4 mixture for Li-ion conductivity by machine learning

TL;DR

This paper uses machine learning to optimize the composition of a ternary Li-ion conductive material, achieving a significant increase in conductivity and demonstrating the method's effectiveness in materials development.

Contribution

Introduces a machine learning approach to optimize ternary mixture compositions for enhanced Li-ion conductivity, surpassing traditional methods.

Findings

Optimal composition identified as 25:14:61 mol% with conductivity 4.9 x 10E-4 S/cm at 300°C

Machine learning effectively predicts compositions that improve conductivity

Enhanced conductivity attributed to coexistence of multiple phases

Abstract

Mixing heterogeneous Li-ion conductive materials is one of potential ways to enhance the Li-ion conductivity more than that of the parent materials. However, the development of the mixtures had not exhibited significant progress because it is a formidable task to cover the vast possible composition of the parent materials using traditional ways. Here, we introduce a fashion based on machine learning to optimize the composition ratio of ternary Li3PO4-Li3BO3-Li2SO4 mixture for its Li-ion conductivity. According to our results, the optimum composition of the ternary mixture system is 25:14:61 (Li3PO4: Li3BO3: Li2SO4 in mol%), whose Li-ion conductivity is measured as 4.9 x 10E-4 S/cm at 300 {\deg}C. Our X-ray structure analysis indicates that Li-ion conductivity in the mixing systems is enhanced by virtue of the coexistence of two or more phases. Although the mechanism enhancing Li-ion conductivity is not simple, our results demonstrate the effectiveness of machine learning for the development of materials.