Data-Driven Viewpoint for Developing Next-Generation Mg-Ion Solid-State Electrolytes
Fangling Yang, Ryuhei Sato, Eric Jianfeng Cheng, Kazuaki Kisu, Qian Wang, Xue Jia, Shin‐ichi Orimo, Hao Li
Abstract
Magnesium (Mg) is a promising alternative to lithium (Li) in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solid-state electrolytes (SSEs) is one of the key challenges that hamper the development of Mg-ion solid-state batteries. Though various Mg-ion SSEs have been reported in recent years, key insights are hard to be derived from a single literature report. Besides, the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs. In this Viewpoints article, we analyze the structural characteristics of the Mg-based SSEs with high ionic conductivity reported in the last four decades based upon data mining - we provide big-data-derived insights into the challenges and opportunities in developing next-generation Mg-ion SSEs.