Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li-Ion Conductivity by Machine Learning

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 °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.