Visual Analysis on Machine Learning Assisted Prediction of Ionic Conductivity for Solid-State Electrolytes

• Main Authors: Shao, Hui, Pu, Jiansu, Zhu, Yanlin, Gao, Boyang, Zhu, Zhengguo, Rao, Yunbo
• Format: Conference Proceeding
• Language: English
• Subjects: Analytical models; Data visualization; Electrolytes; High-dimensional data; Interactive visualization; Ionic conductivity; Liquids; Machine learning; Materials discovery; Solid electrolyte; Visualization
• ISSN: 2165-8773
• DOI: 10.1109/PacificVis52677.2021.00038

Lithium ion batteries (LIBs) are widely used as the important energy sources in our daily life such as mobile phones, electric vehicles, and drones etc. Due to the potential safety risks caused by liquid electrolytes, the experts have tried to replace liquid electrolytes with solid ones. However, it is very difficult to find suitable alternatives materials in traditional ways for its incredible high cost in searching. Machine learning (ML) based methods are currently introduced and used for material prediction. But there is rarely an assisting learning tools designed for domain experts for institutive performance comparison and analysis of ML model. In this case, we propose an interactive visualization system for experts to select suitable ML models, understand and explore the predication results comprehensively. Our system employs a multi-faceted visualization scheme designed to support analysis from the perspective of feature composition, data similarity, model performance, and results presentation. A case study with real experiments in lab has been taken by the expert and the results of confirmed the effectiveness and helpfulness of our system.