Machine learning models for designing next-generation electrolytes for Li batteries


All solid-state batteries using lithium metal as anodes are currently being explored for high power and high energy density batteries. Traditional lithium ion batteries (LIBs) using liquid electrolytes pose significant issues, e.g., the organic electrolytes are flammable and undergo degradation. These issues can be overcome using solid electrolytes such as sulfide-based glassy and glass-ceramic solid electrolytes. Such materials have shown to possess very high ionic conductivities and excellent mechanical properties. The glass structure plays a crucial role in the diffusion characteristics. By developing machine learning models we will identify the superior electrolyte materials.

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