A dynamic database of solid-state electrolyte (DDSE) picturing all-solid-state batteries

Abstract

All-solid-state batteries (ASSBs) are a class of safer and higher-energy-density materials compared to conventional devices, from which solid-state electrolytes (SSEs) are their essential components. To date, investigations to search for high ion-conducting solid-state electrolytes have attracted broad concern. However, obtaining SSEs with high ionic conductivity is challenging due to the complex structural information and the less-explored structure-performance relationship. To provide a solution to these challenges, developing a database containing typical SSEs from available experimental reports would be a new avenue to understand the structure-performance relationships and find out new design guidelines for reasonable SSEs. Herein, a dynamic experimental database containing >600 materials was developed in a wide range of temperatures (132.40–1261.60 ​K), including mono- and divalent cations (e.g., Li+, Na+, K+, Ag+, Ca2+, Mg2+, and Zn2+) and various types of anions (e.g., halide, hydride, sulfide, and oxide). Data-mining was conducted to explore the relationships among different variates (e.g., transport ion, composition, activation energy, and conductivity). Overall, we expect that this database can provide essential guidelines for the design and development of high-performance SSEs in ASSB applications. This database is dynamically updated, which can be accessed via our open-source online system.

Keywords

• Solid-state electrolyte (SSE)
• All-solid-state battery (ASSB)
• Ionic conductivity
• Dynamic database
• Machine learning