Optimization of a Heterogeneous Ternary Li3PO4-Li3BO3-Li2SO4Mixture for Li-Ion Conductivity by Machine Learning

Kenji Homma, Yu Liu, Masato Sumita, Ryo Tamura, Naoki Fushimi, Junichi Iwata, Koji Tsuda, Chioko Kaneta

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Mixing heterogeneous Li-ion conductive materials is one potential way to enhance Li-ion conductivity more than that of the parent materials. However, the huge number of possible compositions of parent materials impedes the development of an optimal mixture by using conventional methods. In this study, we employed machine learning to optimize the composition ratio of ternary Li3PO4-Li3BO3-Li2SO4 for Li-ion conductivity. We found the optimum composition of the ternary mixture system to be 25:14:61 (Li3PO4:Li3BO3:Li2SO4 in mol %), whose Li-ion conductivity is measured as 4.9 × 10-4 S/cm at 300 °C. Our X-ray structure analysis suggested that Li-ion conductivity of the mixed systems tends to be enhanced by 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.

Original languageEnglish
Pages (from-to)12865-12870
Number of pages6
JournalJournal of Physical Chemistry C
Volume124
Issue number24
DOIs
Publication statusPublished - 2020 Jun 18
Externally publishedYes

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Energy(all)
  • Physical and Theoretical Chemistry
  • Surfaces, Coatings and Films

Fingerprint Dive into the research topics of 'Optimization of a Heterogeneous Ternary Li<sub>3</sub>PO<sub>4</sub>-Li<sub>3</sub>BO<sub>3</sub>-Li<sub>2</sub>SO<sub>4</sub>Mixture for Li-Ion Conductivity by Machine Learning'. Together they form a unique fingerprint.

Cite this