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机器学习在设计高性能锂电池正极材料与电解质中的应用
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作者 刘振东 潘嘉杰 刘全兵 《化学进展》 SCIE CAS CSCD 北大核心 2023年第4期577-592,共16页
随着大数据和人工智能的发展以及机器学习(ML)与化学学科领域的交叉,ML技术与电池领域的结合激发了更有前途的电池开发方法,尤其在电池材料设计、性能预测、结构优化等方面的应用愈加广泛。应用ML可以有效地加速电池材料的筛选进程并预... 随着大数据和人工智能的发展以及机器学习(ML)与化学学科领域的交叉,ML技术与电池领域的结合激发了更有前途的电池开发方法,尤其在电池材料设计、性能预测、结构优化等方面的应用愈加广泛。应用ML可以有效地加速电池材料的筛选进程并预测锂电池(LBs)的性能,从而推动LBs的发展。本文简要介绍了ML的基本思想及其在LBs领域中几种重要的ML算法,之后讨论了传统模拟计算方法与ML方法各自的误差表现及分析,借此来提高LBs专家对ML方法的理解。其次,重点介绍了ML在电池材料实际开发中的应用,包括正极材料、电解质、材料多尺度模拟及高通量实验(HTE)等方面,借此介绍ML方法在电池领域应用的思想和手段。最后,总结了ML方法在锂电池领域中的研究现状并展望了其应用前景。本综述旨在阐明ML在LBs开发中的应用,并为先进LBs的研究提供借鉴。 展开更多
关键词 锂电池 机器学习 材料筛选 材料设计 性能预测
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Designing Electrochemical Nanoreactors to Accelerate Li_(2)S_(1/2) Three-Dimensional Growth Process and Generating More Li_(2)S for Advanced Li–S Batteries
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作者 Junhao Li Kaixiang Shi +7 位作者 jiajie pan Junda pan Yongxian Lin Kaixin Wang Hao Li Jinyun Liao Huafeng Dong Quanbing Liu 《Renewables》 2023年第3期341-352,共12页
With advantages of low costs and high energy density,Li–S batteries are considered as one of the most promising energy storage devices.However,Li_(2)S_(2) with a high dissociation energy and insulative properties is ... With advantages of low costs and high energy density,Li–S batteries are considered as one of the most promising energy storage devices.However,Li_(2)S_(2) with a high dissociation energy and insulative properties is hard to convert into Li_(2)S,resulting in underutilization of sulfur capacity.Herein,Co-Mo_(2)C@C yolk–shell spheres as nanoreactors were designed to confront this challenge rationally.The Co-Mo_(2)C@C-induced Li_(2)S_(1/2) nucleation and growth in the three-dimensional process and the cathode produced more Li_(2)S after full discharge.Experimental studies and theoretical calculations reveal that the conversion barrier from Li_(2)S_(2) into Li_(2)S was lowered while the diffusion of lithium ions and electron transfer accelerated when using the Co-Mo_(2)C@C catalyst.Based on the above advantages,the Co-Mo_(2)C@C/S cathode exhibits a high reversible capacity and excellent cyclic stability,such as an initial specific capacity of 1200 mAh g^(−1) at 0.1 C with 709 mAh g^(−1) at 1.0 C after 1000 cycles with a low capacity fading rate of 0.04%per cycle.Even at high densities of 3.0 C and 5.0 C,the specific capacities are 647.6 and 557.7 mAh g^(−1) after 400 cycles,respectively.Impressively,it also shows ca.770 and 900 mAh g^(−1) at 0.2 C after 50 cycles with high sulfur loadings of 4.2 and 5.1 mg cm−2,respectively.The present work may provide new insights into the design of nanoreactors to promote Li_(2)S_(1/2) growth in a three-dimensional process and accelerate conversion from solid Li_(2)S_(2) to solid Li_(2)S in high performance Li–S batteries. 展开更多
关键词 Li-S batteries cobalt-dopedβ-Mo_(2)C yolk-shell structure optimizing Li_(2)S_(1/2)growth process fast solid-solid conversion of Li_(2)S_(2)-Li_(2)S
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