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

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Abstract

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.

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摘要

随着电子消费产品、电动汽车以及电网规模固定能源储存装置的快速发展, 人们迫切需求发展高能量密度电池。锂金属电池被认为是最具有潜力的高能量密度存储设备之一, 并且由于它超高的理论容量、极低的电化学电势以及轻质量而获得研究人员的广泛关注。然而, 锂金属电池的发展仍然有许多挑战, 比如不可控的锂枝晶生长, 体积的动态变化, 界面阻抗, 严重的化学和电化学腐蚀等等, 这些问题不仅导致了锂金属电池的低库伦效率, 同时还导致了内短路的风险, 有可能进一步引发更严重的副反应和安全隐患。不过, 在经过几十年的遗忘之后, 锂金属已经在准备重新复兴, 这得利于研究工具和纳米技术方法的快速发展。在这篇综述里, 近年来的锂金属负极各种材料设计被阐述。通过锂金属负极抑制锂枝晶以及延长电池的使用寿命仍然是挑战。多尺度模型方法被重点关注, 同时还需要推动电极材料发展的应用。基于机器学习技术的混合多尺度模型应用方法被提出, 同时云计算平台也被应用。通过人工智能技术, 锂金属负极的材料设计信息实现整合。最后, 这篇综述提出了一种新的框架用于开发下一代锂金属负极设计方法, 该框架基于多尺度数据驱动模型和机器学习技术为锂金属负极设计提供了数字化解决方案。

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Acknowledgements

This study was financially supported by the National Key R&D Program of China (No. 2017YFB0103700) and National Natural Science Foundation of China (No. U1864213).

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  1. Li-Sheng Zhang and Xin-Lei Gao contributed equally to this work.

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  1. School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China

    Li-Sheng Zhang, Xin-Lei Gao, Xin-Hua Liu, Zheng-Jie Zhang, Rui Cao, Han-Chao Cheng, Ming-Yue Wang, Xiao-Yu Yan & Shi-Chun Yang

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Zhang, LS., Gao, XL., Liu, XH. et al. CHAIN: unlocking informatics-aided design of Li metal anode from materials to applications. Rare Met. 41, 1477–1489 (2022). https://doi.org/10.1007/s12598-021-01925-8

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