These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
V01_20181118.pdf (43.17 MB)

Li-ion Conductive Li3PO4-Li3BO3-Li2SO4 Mixture: Prevision through Density Functional Molecular Dynamics and Machine Learning

submitted on 27.11.2018 and posted on 27.11.2018 by Masato Sumita, Ryo Tamura, Kenji Homma, Chioko Kaneta, Koji Tsuda
The development of high Li-ion conductive solid electrolytes is crucial for the practical use of all solid-state Li-ion batteries. The mixing of hetero Li-ion conductive substances is a known method for enhancing the Li-ion conductivity more than in the original substances. In this study, using computer simulations, we proved that a ternary Li3PO4-Li3BO3-Li2SO4system has the potential to demonstrate improved Li-ion conductivity based on the introduction of a quasi-Li/oxygen vacancy. The Li-ion conductivities of this ternary system were calculated using several model systems based on the density functional molecular dynamics under an isothermal-isobaric ensemble. However, an exploration using the density functional molecular dynamics cannot cover the entire combinatorial space owing to a lack of computational capability. To search through a vast combinatorial space, we conducted analyses using a machine learning technique. The analysis results clarify the relationship between Li-ion conductivity and phonon free energy, and allow the optimum composition ratio with the highest Li-ion conductivity to be predicted.


Email Address of Submitting Author





ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict interest