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|a 10.1109/TVCG.2021.3114812
|2 doi
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|a eng
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|a Pu, Jiansu
|e verfasserin
|4 aut
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|a matExplorer
|b Visual Exploration on Predicting Ionic Conductivity for Solid-state Electrolytes
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|c 2022
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|a Date Revised 27.12.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Lithium ion batteries (LIBs) are widely used as important energy sources for mobile phones, electric vehicles, and drones. Experts have attempted to replace liquid electrolytes with solid electrolytes that have wider electrochemical window and higher stability due to the potential safety risks, such as electrolyte leakage, flammable solvents, poor thermal stability, and many side reactions caused by liquid electrolytes. However, finding suitable alternative materials using traditional approaches is very difficult due to the incredibly high cost in searching. Machine learning (ML)-based methods are currently introduced and used for material prediction. However, learning tools designed for domain experts to conduct intuitive performance comparison and analysis of ML models are rare. In this case, we propose an interactive visualization system for experts to select suitable ML models and understand and explore the predication results comprehensively. Our system uses a multifaceted visualization scheme designed to support analysis from various perspectives, such as feature distribution, data similarity, model performance, and result presentation. Case studies with actual lab experiments have been conducted by the experts, and the final results confirmed the effectiveness and helpfulness of our system
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|a Shao, Hui
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|a Gao, Boyang
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|4 aut
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| 700 |
1 |
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|a Zhu, Zhengguo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Yanlin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Rao, Yunbo
|e verfasserin
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| 700 |
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|a Xiang, Yong
|e verfasserin
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
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|g 28(2022), 1 vom: 29. Jan., Seite 65-75
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|g volume:28
|g year:2022
|g number:1
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|g month:01
|g pages:65-75
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|u http://dx.doi.org/10.1109/TVCG.2021.3114812
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