@article{Yang2024, 
author = {Fang-Ling Yang and Ryuhei Sato and Eric Jianfeng Cheng and Kazuaki Kisu and Qian Wang and Xue Jia and Shin-ichi Orimo and Hao Li},
title = {Data-Driven Viewpoint for Developing Next-Generation Mg-Ion Solid-State Electrolytes},
year = {2024},
journal = {Journal of Electrochemistry},
volume = {30},
number = {7},
pages = {2415001},
keywords = {Data mining, Magnesium-ion solid-state electrolytes, All-solid-state batteries, Magnesium-ion conductivity},
url = {https://www.sciopen.com/article/10.61558/2993-074X.3461},
doi = {10.61558/2993-074X.3461},
abstract = {Magnesium (Mg) is a promising alternative to lithium (Li) as an anode material in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solidstate 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 viewpoint 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.}
}