CHAIN: unlocking informatics-aided design of Li metal anode from materials to applications

被引:53
作者
Zhang, Li-Sheng [1 ]
Gao, Xin-Lei [1 ]
Liu, Xin-Hua [1 ]
Zhang, Zheng-Jie [1 ]
Cao, Rui [1 ]
Cheng, Han-Chao [1 ]
Wang, Ming-Yue [1 ]
Yan, Xiao-Yu [1 ]
Yang, Shi-Chun [1 ]
机构
[1] Beihang Univ, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Li metal anodes; Energy materials; Multi-scale modeling; Fusion of model and data; Digital twin; CHAIN; LITHIUM-ION; BATTERIES; ELECTROLYTE; SURFACE; MODEL;
D O I
10.1007/s12598-021-01925-8
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With the rapid development of consumer electronics, electric vehicles and grid-scale stationary energy storage, high-energy batteries are urgently demanded at present. Lithium metal batteries (LMBs) are considered to be one of the most promising high-energy density energy storage devices at present and have received much attention due to their ultra-high theoretical capacity, extremely low electrochemical potential and light mass. However, critical issues, such as uncontrollable lithium dendrite growth, dynamic changes in volume, interfacial impedance, severe chemical and electrochemical corrosion, remain huge challenges for Li metal anodes, which not only lead to low Columbic efficiency of LMBs, but also pose the risk of internal short circuit, causing serious side reactions and safety concerns that hinder LMBs from practical applications. Nevertheless, lithium metal is gradually poised for a revival after decades of oblivion, due to the development of research tools and nanotechnology-based solutions. In this review, various recent material designs for lithium metal anodes are reviewed based on previous theoretical understanding and analysis. Suppressing Li dendrites and ensuring the long life span of practical batteries through limited Li metal anodes design are still challenges. Multi-scale modeling methods are concerned, requiring the application of electrode material development. Hybrid multi-scale modeling application methods with machine learning technology are proposed based on the cloud computing platform. Computational material designs for Li metal anodes on model information are integrated with artificial intelligence. Finally, this review provides a novel framework for next-generation lithium metal anode design methods with a digital solution based on multi-scale data-driven models and machine learning techniques.
引用
收藏
页码:1477 / 1489
页数:13
相关论文
共 81 条
  • [1] Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
    Ahmad, Zeeshan
    Xie, Tian
    Maheshwari, Chinmay
    Grossman, Jeffrey C.
    Viswanathan, Venkatasubramanian
    [J]. ACS CENTRAL SCIENCE, 2018, 4 (08) : 996 - 1006
  • [2] Identifying Capacity Limitations in the Li/Oxygen Battery Using Experiments and Modeling
    Albertus, Paul
    Girishkumar, G.
    McCloskey, Bryan
    Sanchez-Carrera, Roel S.
    Kozinsky, Boris
    Christensen, Jake
    Luntz, A. C.
    [J]. JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 2011, 158 (03) : A343 - A351
  • [3] Closed-loop optimization of fast-charging protocols for batteries with machine learning
    Attia, Peter M.
    Grover, Aditya
    Jin, Norman
    Severson, Kristen A.
    Markov, Todor M.
    Liao, Yang-Hung
    Chen, Michael H.
    Cheong, Bryan
    Perkins, Nicholas
    Yang, Zi
    Herring, Patrick K.
    Aykol, Muratahan
    Harris, Stephen J.
    Braatz, Richard D.
    Ermon, Stefano
    Chueh, William C.
    [J]. NATURE, 2020, 578 (7795) : 397 - +
  • [4] New insights into the interactions between electrode materials and electrolyte solutions for advanced nonaqueous batteries
    Aurbach, D
    Markovsky, B
    Levi, MD
    Levi, E
    Schechter, A
    Moshkovich, M
    Cohen, Y
    [J]. JOURNAL OF POWER SOURCES, 1999, 81 : 95 - 111
  • [5] Machine learning for continuous innovation in battery technologies
    Aykol, Muratahan
    Herring, Patrick
    Anapolsky, Abraham
    [J]. NATURE REVIEWS MATERIALS, 2020, 5 (10) : 725 - 727
  • [6] Interfaces and Interphases in All-Solid-State Batteries with Inorganic Solid Electrolytes
    Banerjee, Abhik
    Wang, Xuefeng
    Fang, Chengcheng
    Wu, Erik A.
    Meng, Ying Shirley
    [J]. CHEMICAL REVIEWS, 2020, 120 (14) : 6878 - 6933
  • [7] Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030
    Berckmans, Gert
    Messagie, Maarten
    Smekens, Jelle
    Omar, Noshin
    Vanhaverbeke, Lieselot
    Van Mierlo, Joeri
    [J]. ENERGIES, 2017, 10 (09)
  • [8] Atomic Layer Deposition of LixAlyS Solid-State Electrolytes for Stabilizing Lithium-Metal Anodes
    Cao, Yanqiang
    Meng, Xiangbo
    Elam, Jeffrey W.
    [J]. CHEMELECTROCHEM, 2016, 3 (06): : 858 - 863
  • [9] A Critical Review of Machine Learning of Energy Materials
    Chen, Chi
    Zuo, Yunxing
    Ye, Weike
    Li, Xiangguo
    Deng, Zhi
    Ong, Shyue Ping
    [J]. ADVANCED ENERGY MATERIALS, 2020, 10 (08)
  • [10] Recent Advances in Energy Chemistry between Solid-State Electrolyte and Safe Lithium-Metal Anodes
    Cheng, Xin-Bing
    Zhao, Chen-Zi
    Yao, Yu-Xing
    Liu, He
    Zhang, Qiang
    [J]. CHEM, 2019, 5 (01): : 74 - 96