Screening and Predication of Solid Electrolyte Based on Visualization

doi:10.11871/jfdc.issn.2096-742X.2021.04.002

Abstract

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

PU Jiansu,ZHU Zhengguo,SHAO Hui,GAO Boyang,ZHU Yanlin,YAN Zongkai,XIANG Yong. Screening and Predication of Solid Electrolyte Based on Visualization[J]. Frontiers of Data and Computing, 2021, 3(4): 18-29.

Figures/Tables 10

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References 19

[1]	黄彦瑜. 锂电池发展简史[J]. 物理, 2007,36(08):643-651.
[2]	Wang X, Lu X, Liu B, et al. Flexible energy‐storage devices: design consideration and recent progress[J]. Advanced materials, 2014,26(28):4763-4782. doi: 10.1002/adma.v26.28
[3]	闫金定. 锂离子电池发展现状及其前景分析[J]. 航空学报, 2014,35(10):2767-2775.
	闫金定. 锂离子电池发展现状及其前景分析[J]. 航空学报, 2014,35(10):2767-2775.
[4]	张建军, 董甜甜, 杨金凤, 等. 全固态聚合物锂电池的科研进展, 挑战与展望[J]. 储能科学与技术, 2018,7(5):861-868.
	张建军, 董甜甜, 杨金凤, 等. 全固态聚合物锂电池的科研进展, 挑战与展望[J]. 储能科学与技术, 2018,7(5):861-868.
[5]	姜鹏峰, 石元盛, 李康万, 等. 固态电解质锂镧锆氧 (LLZO) 的研究进展[J]. 储能科学与技术, 2020,9(2):523.
	姜鹏峰, 石元盛, 李康万, 等. 固态电解质锂镧锆氧 (LLZO) 的研究进展[J]. 储能科学与技术, 2020,9(2):523.
[6]	Sendek A D, Yang Q, Cubuk E D, et al. Holistic compu-tational structure screening of more than 12000 candidates for solid lithium-ion conductor materials[J]. Energy & Environmental Science, 2017,10(1):306-320.
	Sendek A D, Yang Q, Cubuk E D, et al. Holistic compu-tational structure screening of more than 12000 candidates for solid lithium-ion conductor materials[J]. Energy & Environmental Science, 2017,10(1):306-320.
[7]	Pham H Q, Lee H Y, Hwang E H, et al. Non-flammable organic liquid electrolyte for high-safety and high-energy density Li-ion batteries[J]. Journal of Power Sources, 2018,404:13-19. doi: 10.1016/j.jpowsour.2018.09.075
	Pham H Q, Lee H Y, Hwang E H, et al. Non-flammable organic liquid electrolyte for high-safety and high-energy density Li-ion batteries[J]. Journal of Power Sources, 2018,404:13-19. doi: 10.1016/j.jpowsour.2018.09.075
[8]	Haregewoin A M, Wotango A S, Hwang B J. Electrolyte additives for lithium ion battery electrodes: progress and perspectives[J]. Energy & Environmental Science, 2016,9(6):1955-1988.
	Haregewoin A M, Wotango A S, Hwang B J. Electrolyte additives for lithium ion battery electrodes: progress and perspectives[J]. Energy & Environmental Science, 2016,9(6):1955-1988.
[9]	Zhang S, Li J, Jiang N, et al. Rational Design of an Ionic Liquid‐Based Electrolyte with High Ionic Conductivity Towards Safe Lithium/Lithium‐Ion Batteries[J]. Chemistry-An Asian Journal, 2019,14(16):2810-2814.
	Zhang S, Li J, Jiang N, et al. Rational Design of an Ionic Liquid‐Based Electrolyte with High Ionic Conductivity Towards Safe Lithium/Lithium‐Ion Batteries[J]. Chemistry-An Asian Journal, 2019,14(16):2810-2814.
[10]	Sun C, Liu J, Gong Y, et al. Recent advances in all-solid-state rechargeable lithium batteries[J]. Nano Energy, 2017,33:363-386. doi: 10.1016/j.nanoen.2017.01.028
	Sun C, Liu J, Gong Y, et al. Recent advances in all-solid-state rechargeable lithium batteries[J]. Nano Energy, 2017,33:363-386. doi: 10.1016/j.nanoen.2017.01.028
[11]	Liu Q, Geng Z, Han C, et al. Challenges and perspectives of garnet solid electrolytes for all solid-state lithium batteries[J]. Journal of Power Sources, 2018,389:120-134. doi: 10.1016/j.jpowsour.2018.04.019
	Liu Q, Geng Z, Han C, et al. Challenges and perspectives of garnet solid electrolytes for all solid-state lithium batteries[J]. Journal of Power Sources, 2018,389:120-134. doi: 10.1016/j.jpowsour.2018.04.019
[12]	Meredig B, Agrawal A, Kirklin S, et al. Combinatorial screening for new materials in unconstrained composition space with machine learning[J]. Physical Review B, 2014,89(9):094-104.
	Meredig B, Agrawal A, Kirklin S, et al. Combinatorial screening for new materials in unconstrained composition space with machine learning[J]. Physical Review B, 2014,89(9):094-104.
[13]	Shandiz M A, Gauvin R. Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries[J]. Computational Materials Science, 2016,117:270-278. doi: 10.1016/j.commatsci.2016.02.021
	Shandiz M A, Gauvin R. Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries[J]. Computational Materials Science, 2016,117:270-278. doi: 10.1016/j.commatsci.2016.02.021
[14]	Cubuk E D, Sendek A D, Reed E J. Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data[J]. The Journal of chemical physics, 2019,150(21):214701. doi: 10.1063/1.5093220
	Cubuk E D, Sendek A D, Reed E J. Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data[J]. The Journal of chemical physics, 2019,150(21):214701. doi: 10.1063/1.5093220
[15]	Chen A, Zhang X, Zhou Z. Machine learning: accelerat-ing materials development for energy storage and conver-sion[J]. InfoMat, 2020,2(3):553-576. doi: 10.1002/inf2.v2.3
	Chen A, Zhang X, Zhou Z. Machine learning: accelerat-ing materials development for energy storage and conver-sion[J]. InfoMat, 2020,2(3):553-576. doi: 10.1002/inf2.v2.3
[16]	Ganuza M L, Ferracutti G, Gargiulo M F, et al. The spinel explorer—interactive visual analysis of spinel group minerals[J]. IEEE transactions on visualization and computer graphics, 2014,20(12):1913-1922. doi: 10.1109/TVCG.2014.2346754
	Ganuza M L, Ferracutti G, Gargiulo M F, et al. The spinel explorer—interactive visual analysis of spinel group minerals[J]. IEEE transactions on visualization and computer graphics, 2014,20(12):1913-1922. doi: 10.1109/TVCG.2014.2346754
[17]	Bernard J, Sessler D, Kohlhammer J, et al. Using dashboard networks to visualize multiple patient histories: a design study on post-operative prostate cancer[J]. IEEE transac-tions on visualization and computer graphics, 2018,25(3):1615-1628.
	Bernard J, Sessler D, Kohlhammer J, et al. Using dashboard networks to visualize multiple patient histories: a design study on post-operative prostate cancer[J]. IEEE transac-tions on visualization and computer graphics, 2018,25(3):1615-1628.
[18]	Sun D, Feng Z, Chen Y, et al. DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Fore-casting[C]//Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020: 1-13.
	Sun D, Feng Z, Chen Y, et al. DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Fore-casting[C]//Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020: 1-13.
[19]	Esaka T, Greenblatt M. Lithium ion conduction in substi-tuted Li5GaO4 phases[J]. Solid state ionics, 1986,21(3):255-261. doi: 10.1016/0167-2738(86)90080-9
	Esaka T, Greenblatt M. Lithium ion conduction in substi-tuted Li5GaO4 phases[J]. Solid state ionics, 1986,21(3):255-261. doi: 10.1016/0167-2738(86)90080-9