Frontiers of Data and Domputing ›› 2021, Vol. 3 ›› Issue (4): 18-29.doi: 10.11871/jfdc.issn.2096-742X.2021.04.002

• Special Issue: Visualization and Visual Analysis • Previous Articles     Next Articles

Screening and Predication of Solid Electrolyte Based on Visualization

PU Jiansu1,*(),ZHU Zhengguo1(),SHAO Hui1(),GAO Boyang1(),ZHU Yanlin2(),YAN Zongkai3(),XIANG Yong3()   

  1. 1. Big Data Visual Analysis Lab, University of Electronic Science and Technology, Chengdu, Sichuan 610000, China
    2. Clean Energy Research Institute, Shenzhen 518048, China
    3. Material Genome Engineering Research Center, School of Materials and Energy, University of Electronic Science and Technology, Chengdu, Sichuan 610000, China
  • Received:2021-06-10 Online:2021-08-20 Published:2021-08-30
  • Contact: PU Jiansu E-mail:jiansu.pu@foxmail.com;202022080208@std.uestc.edu.cn;sophyond@163.com;202052080209@std.uestc.edu.cn;zhuyanlin@uceri.com;yanzongkai@uestc.edu.cn;xyg@uestc.edu.cn

Abstract:

[Objective] It is a hot research topic to find the ideal solid electrolyte material with high ion conductivity, and replace the liquid electrolyte which has safety concerns as the electrolyte material of lithium batteries. [Context] In recent years, methods such as machine learning have been widely used in the prediction of new materials. However, there are few aids to help materials experts analyze and understand machine learning models to predict the composition of materials that meet performance requirements. [Methods] Under such background, we built a visual analysis system based on visualization technology, trying to help experts in the field of materials analyze the results of machine learning, predict and look for high-performance solid electrolyte materials. [Results] We compare the results of several machine learning algorithms and use visualization techniques to display the results. We visually analyze the relationship between materials through different views and finally give the prediction based on some cases we summarized. [Conclusions] Many material experiments have verified the excellent properties of some predicted materials and have confirmed the effectiveness of our system.

Key words: visual analysis system, machine learning, ionic conductivity, material discovery, solid electrolyte, visual analysis system, machine learning, ionic conductivity, material discovery, solid electrolyte