Liu Xinyan, Zou Bo-Bo, Wang Ya-Nan, Chen Xiang, Huang Jia-Qi, Zhang Xue-Qiang, Zhang Qiang, Peng Hong-Jie
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.
Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.
J Am Chem Soc. 2024 Dec 4;146(48):33012-33021. doi: 10.1021/jacs.4c09363. Epub 2024 Oct 25.
Lithium metal batteries (LMBs) with high energy density are perceived as the most promising candidates to enable long-endurance electrified transportation. However, rapid capacity decay and safety hazards have impeded the practical application of LMBs, where the entangled complex degradation pattern remains a major challenge for efficient battery design and engineering. Here, we present an interpretable framework to learn the accelerated aging of LMBs with a comprehensive data space containing 79 cells varying considerably in battery chemistries and cell parameters. Leveraging only data from the first 10 cycles, this framework accurately predicts the knee points where aging starts to accelerate. Leaning on the framework's interpretability, we further elucidate the critical role of the last 10%-depth discharging on LMB aging rate and propose a universal descriptor based solely on early cycle electrochemical data for rapid evaluation of electrolytes. The machine learning insights also motivate the design of a dual-cutoff discharge protocol, which effectively extends the cycle life of LMBs by a factor of up to 2.8.
具有高能量密度的锂金属电池（LMB）被视为实现长续航电动运输最有前景的候选者。然而，快速的容量衰减和安全隐患阻碍了LMB的实际应用，其复杂的退化模式仍然是高效电池设计和工程面临的主要挑战。在此，我们提出了一个可解释的框架，用于通过包含79个电池的综合数据空间来研究LMB的加速老化，这些电池在电池化学组成和电池参数方面有很大差异。仅利用前10个循环的数据，该框架就能准确预测老化开始加速的拐点。基于该框架的可解释性，我们进一步阐明了最后10%深度放电对LMB老化速率的关键作用，并提出了一个仅基于早期循环电化学数据的通用描述符，用于快速评估电解质。机器学习的见解还推动了双截止放电协议的设计，该协议有效地将LMB的循环寿命延长了高达2.8倍。
