English
往期目录
新闻公告
More
化学进展 2023, Vol. 35 Issue (4): 577-592 DOI: 10.7536/PC220937 前一篇   后一篇

• 综述 •

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

刘振东1, 潘嘉杰1, 刘全兵1,2,*()   

  1. 1.广州市清洁交通能源重点实验室 广东省植物资源生物炼制重点实验室 广东工业大学轻工化工学院 广州 510006
    2.化学与精细化工广东省实验室揭阳分中心 揭阳 515200
  • 收稿日期:2022-10-02 修回日期:2022-12-18 出版日期:2023-04-24 发布日期:2023-02-15
  • 作者简介:

    刘全兵 广东工业大学轻工化工学院,教授,博士生导师。广东省“青年珠江学者”,广州市“珠江科技新星”,具有较丰富的锂离子电池工程开发经验。近年来重点开展电化学能源存储和转换方面的新能源材料与器件研究,涉及锂/锂离子/锂硫电池、超级电容器、电催化/燃料电池等新型化学电源,主持开发了多款型号的锂离子电池产品,并得到了实际应用。迄今为止,在Angew. Chem. Int. Ed., Adv. Mater., Adv. Energy Mater., Adv. Funct. Mater., AIChE J, Small, Appl. Cata. B等国际知名发表SCI学术论文100多篇,申请国内发明专利40多项,授权12项。

  • 基金资助:
    广东省重点领域研发计划(2020B090919005); 国家自然科学基金(22179025); 国家自然科学基金(21905056); 国家自然科学基金(21975056)
Richhtml ( 72 ) 下载PDF ( 914 ) 引用
导出

Application of Machine Learning in the Design of Cathode Materials and Electrolytes for High-Performance Lithium Batteries

Zhendong Liu1, Jiajie Pan1, Quanbing Liu1,2()   

  1. 1. Guangzhou Key Laboratory of Clean Transportation Energy Chemistry, Guangdong Provincial Key Laboratory of Plant Resources Biorefinery, School of Chemical Engineering and Light Industry, Guangdong University of Technology,Guangzhou 510006, China
    2. Jieyang Branch of Chemistry and Chemical Engineering Guangdong Laboratory,Jieyang 515200, China
  • Received:2022-10-02 Revised:2022-12-18 Online:2023-04-24 Published:2023-02-15
  • Contact: *e-mail: liuqb@gdut.edu.cn (Quanbing Liu)
  • Supported by:
    Key-Area Research and Development Program of Guangdong Province(2020B090919005); National Natural Science Foundation of China(22179025); National Natural Science Foundation of China(21905056); National Natural Science Foundation of China(21975056)

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。

关键词： 锂电池, 机器学习, 材料筛选, 材料设计, 性能预测

The rapid application of big data and artificial intelligence, and the deep intersection of machine learning (ML) and chemistry disciplines have inspired more promising development approaches for the integration of ML technology with battery materials, especially in the material design of battery, performance prediction, structure optimization, and so on. The application of ML can effectively accelerate the selection process of battery materials and predict the performance of lithium batteries (LBs), consequently driving the development of LBs. This review briefly introduces the basic idea of ML and several important ML algorithms in the field of LBs, then the error performance and analysis of the traditional simulation calculation method and ML method are discussed, thereby increasing understanding of ML methods by LBs experts. Secondly, the application of ML in the practical development of battery materials, including cathode materials, electrolytes, multi-scale simulation of materials and high-throughput experiments (HTE), is emphatically introduced to draw out the ideas and means of applying ML methods in the field of batteries. Finally, the recent works of ML in lithium batteries are summarized and their application prospects are foreseen. It is hoped that this review will shed light on the application of ML in the development of LBs and promote the development of advanced LBs.

Key words: lithium battery, machine learning, material screening, material design, performance prediction
()
图1 (a)晶体图的构建。晶体被转换为图形,节点代表晶胞中的原子,边代表原子连接。(b)晶体图之上的卷积神经网络结构。每个节点代表每个原子的局部环境[16]
Fig.1 (a) Construction of the crystal graph. Crystals are converted to graphs with nodes representing atoms in the unit cell and edges representing atom connections. (b) Structure of the convolutional neural network on top of the crystal graph. with Each node representing the local environment of each atom. Copyright 2018, PRL
图2 ML在电池领域的算法分类及常用算法
Fig.2 Algorithm classification and common algorithms of ML in the field of batteries
图3 用于监督/非监督和分类/回归方法的ML方法的总体工作原理[25]
Fig.3 The overall working principle of the ML method for supervised/unsupervised and classification/regression methods[25]. Copyright 2021, ACS
表1 常用的机器学习算法优势
Table 1 Common machine learning algorithm advantages and its disadvantages
Method Category Features
Artificial neural network Regression Requires a large amount of data, relatively strong self-learning and fault tolerance, can analyze complex linear relationships, but the interpretability is weak
Linear regression Regression First make the assumption that the data set requires linear consistency, faster modeling, and good interpretability
Ridge Return Regression Can handle non-linear data, but the prediction efficiency decreases when the data volume is particularly large
Polynomial regression Regression Rapid modeling, effective for small data volumes and simple relationships, difficult to accurately represent high-dimensional complex data
Support vector classification Classification Also known as the maximum margin classifier, it is an important classification model that is mostly applicable to binary data
K-Nearest Neighbor Classification Suitable for multi-classification models, but the computational effort is larger compared to other algorithms, and the data set samples are more demanding
Decision Trees Classification Can handle data with missing attributes, good interpretability, but prone to overfitting
Random Forest Classification Not only does it have the advantages of decision trees, but it also prevents overfitting
K-Means clustering Clustering It is a classical clustering algorithm with simple and fast features, but the algorithm requires high quality for the initial data set
Hierarchical Cluster Analysis Clustering By building a hierarchy of clusters, the whole clustering process can be done at once, but it is computationally intensive
图4 一些最常见的ML算法的示意图。 (a)神经网络;(b)K-最近邻;(c)支持向量机;(d)线性回归
Fig.4 Schematics of some of the most common ML algorithms. (a) Neural networks; (b) K-nearest neighbors; (c) support vector machines; (d) linear regression
图5 结合能与DFT和ML的相关图,以及误差分布直方图,(a), (c), (e) 基于从头开始算法,(b), (d), (f)基于迁移学习算法[59]
Fig.5 Combining the correlation plots of energy with DFT and ML, and the histogram of error distribution, (a), (c), (e) are based on ab initio algorithm, (b), (d), (f) are based on migration learning algorithm[59]. Copyright 2021, ESM
图6 训练数据集。x轴是距离加权的Steinhart阶参数(OP),y轴是每个配置的密度。彩色点对应于(LiCl)1-x(ZrCl4)x的不同x[70]
Fig.6 Training data set. The x-axis is a distance-weighted Steinhart order parameter (OP), the y-axis is the density of each configuration. The colored points correspond to different x of (LiCl)1-x(ZrCl4)x[70]. Copyright 2022, Nano Lett
图7 实验流程图。通过制备不同的浆料获取数据集,然后借助不同的ML算法对不同属性的样本进行分类,最后得到理想的固态电解质薄膜[71]
Fig.7 Experimental flowchart. Data sets are obtained by preparing different slurries, and then samples with different properties are classified with the help of different ML algorithms, and finally an ideal solid electrolyte film is obtained[71]. Copyright 2021, ACS
图8 使用机器学习构建力场模型的工作流程[91]。 (a)分子动力学轨迹中进行采样的示意图;(b)构建机器学习力场中产生数据集的示意图;(c)应用示意图;(d)分子动力学采样的原子间距离分布
Fig.8 Workflow for constructing a force field model using machine learning[91]. (a) Schematic of sampling from molecular dynamics trajectories; (b) schematic of the resulting dataset in constructing a machine learning force field; (c) schematic of the application;(d) distribution of interatomic distances sampled by molecular dynamics. Copyright 2020, Rev. Phys. Chem
图9 Otto系统组成部分[97]。 (a)测试系统的单线流程图;(b)系统组成部分的图形表示
Fig.9 Otto system components[97]. (a) Single line flow diagram of test system; (b) graphical representation of system components. Copyright 2019, ECS
[1]
Tian Y S, Zeng G B, Rutt A, Shi T, Kim H, Wang J Y, Koettgen J, Sun Y Z, Ouyang B, Chen T N, Lun Z Y, Rong Z Q, Persson K, Ceder G. Chem. Rev., 2021, 121(3): 1623.

doi: 10.1021/acs.chemrev.0c00767     URL    
[2]
Tong Q C, Gao P Y, Liu H Y, Xie Y, Lv J, Wang Y C, Zhao J J. J. Phys. Chem. Lett., 2020, 11(20): 8710.

doi: 10.1021/acs.jpclett.0c02357     URL    
[3]
Paier J, Hirschl R, Marsman M, Kresse G. J. Chem. Phys., 2005, 122(23): 234102.

doi: 10.1063/1.1926272     URL    
[4]
Hadjadj-Aoul Y, Ait-Chellouche S. Information, 2020, 11(11): 541.

doi: 10.3390/info11110541     URL    
[5]
Dieb T M, Hou Z F, Tsuda K. J. Chem. Phys., 2018, 148(24): 241716.

doi: 10.1063/1.5018065     URL    
[6]
Houchins G, Viswanathan V. J. Chem. Phys., 2020, 153(5): 054124.

doi: 10.1063/5.0015872     URL    
[7]
Hanssens D, Van De Vijver E, Waegeman W, Everett M E, Moffat I, Sarris A, De Smedt P. Surf. Geophys., 2021, 19(5): 541.
[8]
Jiang S, Wu C C, Li F, Zhang Y Q, Zhang Z H, Zhang Q H, Chen Z J, Qu B, Xiao L X, Jiang M L. Rare Met., 2021, 40(7): 1698.

doi: 10.1007/s12598-020-01579-y    
[9]
Zanca F, Glasby L T, Chong S, Chen S Y, Kim J, Fairen-Jimenez D, Monserrat B, Moghadam P Z. J. Mater. Chem., 2021, 9(39): 13584.
[10]
Sun X, Zheng J N, Gao Y J, Qiu C L, Yan Y L, Yao Z H, Deng S W, Wang J G. Appl. Surf. Sci., 2020, 526: 146522.

doi: 10.1016/j.apsusc.2020.146522     URL    
[11]
Cunha R P, Lombardo T, Primo E N, Franco A A. Batter. Supercaps., 2020, 3(1): 60.

doi: 10.1002/batt.v3.1     URL    
[12]
Hu Z H, Jing Y K, Xue Y, Fan P H, Wang L R, Vanyukov M, Kirisci L, Wang J M, Tarter R E, Xie X Q. Drug Alcohol Dependence., 2020, 206: 107604.

doi: 10.1016/j.drugalcdep.2019.107604     URL    
[13]
Delnevo G, Di Lena P, Mirri S, Prandi C, Salomoni P. J. Big Data., 2019, 6(1): 64.

doi: 10.1186/s40537-019-0226-z    
[14]
Nyshadham C, Rupp M, Bekker B, Shapeev A V, Mueller T, Rosenbrock C W, Csányi G, Wingate D W, Hart G L W. Npj Comput. Mater., 2019, 5: 51.

doi: 10.1038/s41524-019-0189-9    
[15]
Venugopal P, Shankar S S, Jebakumar C P, Agarwal R, Alhelou H H, Reka S S, Golshan M E H. IEEE Access., 2021, 9: 159616.

doi: 10.1109/ACCESS.2021.3130994     URL    
[16]
Xie T, Grossman J C. Phys. Rev. Lett., 2018, 120(14): 145301.

doi: 10.1103/PhysRevLett.120.145301     URL    
[17]
Sendek A D, Ransom B, Cubuk E D, Pellouchoud L A, Nanda J, Reed E J. Adv. Energy Mater., 2022, 12(31): 2200553.

doi: 10.1002/aenm.v12.31     URL    
[18]
Schmidt J, Marques M R G, Botti S, Marques M A L. Npj Comput. Mater., 2019, 5: 83.

doi: 10.1038/s41524-019-0221-0    
[19]
Butler K T, Davies D W, Cartwright H, Isayev O, Walsh A. Nature, 2018, 559(7715): 547.

doi: 10.1038/s41586-018-0337-2    
[20]
Zou Z N, Zhang C X, Wang Q Y, Hou Z Z, Xiong Z Q, Kong F, Wang Q J, Song J Z, Liu B Y, Liu B F, Wang L J, Lai F N, Fan Q, Tao W R, Zhao S, Ma X N, Li M, Wu K L, Zhao H, Chen Z J, Xie W. Science, 2022, 378(6615): eabo7923.
[21]
Kalinich M, Ebrahim S, Hays R, Melcher J, Vaidyam A, Torous J. Schizophr. Res. Cogn., 2022, 27: 100216.
[22]
Wu J D, Wei Z B, Liu K L, Quan Z Y, Li Y W. IEEE Trans. Veh. Technol., 2020, 69(11): 12786.

doi: 10.1109/TVT.25     URL    
[23]
Li S Q, He H W, Li J W. Appl. Energy., 2019, 242: 1259.

doi: 10.1016/j.apenergy.2019.03.154     URL    
[24]
GlÓria A F X, Sebastião P J A. IEEE Access., 2021, 9: 75021.

doi: 10.1109/ACCESS.2021.3081794     URL    
[25]
Lombardo T, Duquesnoy M, El-Bouysidy H, ÅrÉn F, Gallo-Bueno A, JØrgensen P B, Bhowmik A, Demortière A, Ayerbe E, Alcaide F, Reynaud M, Carrasco J, Grimaud A, Zhang C, Vegge T, Johansson P, Franco A A. Chem. Rev., 2022, 122(12): 10899.

doi: 10.1021/acs.chemrev.1c00108     URL    
[26]
Guo H Y, Wang Q, Stuke A, Urban A, Artrith N. Front. Energy Res., 2021, 9: 695902.

doi: 10.3389/fenrg.2021.695902     URL    
[27]
Ying Y R, Fan K, Luo X, Qiao J L, Huang H T. J. Mater. Chem. A., 2021, 9(31): 16860.

doi: 10.1039/D1TA04256D     URL    
[28]
Shao H, Pu J S, Zhu Y L, Gao B Y, Zhu Z G, Rao Y B, IEEE 14th PacificVis., 2021, pp. 1-5.
[29]
Hu X S, Che Y H, Lin X K, Onori S. IEEE Trans. Transp. Electrific., 2021, 7(2): 382.

doi: 10.1109/TTE.2020.3017090     URL    
[30]
Zhou Z H. Natl. Sci. Rev., 2018, 5(1): 44.

doi: 10.29394/Scientific.issn.2542-2987.2020.5.15.2.44-66     URL    
[31]
Stern M, Hexner D, Rocks J W, Liu A J. Phys. Rev. X., 2021, 11(2): 021045.
[32]
Tang T, Yuan H M. J. Power Sources., 2021, 514: 230572.

doi: 10.1016/j.jpowsour.2021.230572     URL    
[33]
Moses I A, Joshi R P, Ozdemir B, Kumar N, Eickholt J, Barone V. ACS Appl. Mater. Interfaces., 2021, 13(45): 53355.

doi: 10.1021/acsami.1c04627     URL    
[34]
Wu B, Han S, Shin K G, Lu W. J. Power Sources., 2018, 395: 128.

doi: 10.1016/j.jpowsour.2018.05.040     URL    
[35]
Abdelmoula R, Khamis A, Karray F. Lecture Notes in Computer Science. Cham: Springer International Publishing., 2019, 134.
[36]
Ryan K, Lengyel J, Shatruk M. J. Am. Chem. Soc., 2018, 140(32): 10158.

doi: 10.1021/jacs.8b03913     URL    
[37]
Fioribello S, Giribone P G. J. Finan. Eng., 2018, 5(4): 1850031.

doi: 10.1142/S2424786318500317     URL    
[38]
Tameemi A Q. IEEE Sens. J., 2022, 22(1): 795.

doi: 10.1109/JSEN.2021.3131859     URL    
[39]
Guo H S, Wang W J. Artif. Intell. Rev., 2019, 51(1): 19.

doi: 10.1007/s10462-017-9555-5    
[40]
Vidal C, Malysz P, Kollmeyer P, Emadi A. IEEE Access., 2020, 8: 52796.

doi: 10.1109/Access.6287639     URL    
[41]
Sutton C, Boley M, Ghiringhelli L M, Rupp M, Vreeken J, Scheffler M. Nat. Commun., 2020, 11: 4428.

doi: 10.1038/s41467-020-17112-9    
[42]
Liow C H, Kang H, Kim S, Na M, Lee Y J, Baucour A, Bang K, Shim Y, Choe J, Hwang G, Cho S, Park G, Yeom J, Agar J C, Yuk J M, Shin J, Lee H M, Byon H R, Cho E, Hong S. Nano Energy., 2022, 98: 107214.

doi: 10.1016/j.nanoen.2022.107214     URL    
[43]
Jürgen Hafner C W G C. MRS Bulletin., 2006, (31): 659.
[44]
Faber F A, Hutchison L, Huang B, Gilmer J, Schoenholz S S, Dahl G E, Vinyals O, Kearnes S, Riley P F, von Lilienfeld O A. J. Chem. Theory Comput., 2017, 13(11): 5255.

doi: 10.1021/acs.jctc.7b00577     URL    
[45]
Jha D, Choudhary K, Tavazza F, Liao W K, Choudhary A, Campbell C, Agrawal A. Nat. Commun., 2019, 10: 5316.

doi: 10.1038/s41467-019-13297-w    
[46]
Hruska E, Gale A, Liu F. J. Chem. Theory Comput., 2022, 18(2): 1096.

doi: 10.1021/acs.jctc.1c01040     pmid: 34991320
[47]
Wang G Y, Fearn T, Wang T Y, Choy K L. ACS Cent. Sci., 2021, 7(9): 1551.

doi: 10.1021/acscentsci.1c00611     URL    
[48]
Wu W, Sun Q. ACS Mater. Lett., 2022, 4(1): 175.
[49]
Jin L J, Ji Y J, Wang H S, Ding L F, Li Y Y. Phys. Chem. Chem. Phys., 2021, 23(38): 21470.

doi: 10.1039/D1CP02963K     URL    
[50]
Meuwly M. Chem. Rev., 2021, 121(16): 10218.

doi: 10.1021/acs.chemrev.1c00033     URL    
[51]
Leverant C J, Harvey J A, Alam T M, Greathouse J A. J. Phys. Chem., 2021, 125(46): 25898.
[52]
Allam O, Cho B W, Kim K C, Jang S S. RSC Adv., 2018, 8(69): 39414.

doi: 10.1039/C8RA07112H     URL    
[53]
Moosavi S M, Chidambaram A, Talirz L, Haranczyk M, Stylianou K C, Smit B. Nat. Commun., 2019, 10: 539.
[54]
Okubo M, Ko S, Dwibedi D, Yamada A. J. Mater. Chem., 2021, 9(12): 7407.
[55]
Chkirbene Z, Erbad A, Hamila R, Mohamed A, Guizani M, Hamdi M. IEEE Access., 2020, 8: 95864.

doi: 10.1109/Access.6287639     URL    
[56]
Zhou L M, Yao A M, Wu Y J, Hu Z Y, Huang Y H, Hong Z J. Adv. Theory Simul., 2021, 4(9): 2100196.

doi: 10.1002/adts.v4.9     URL    
[57]
Xu S Q, Liang J C, Yu Y D, Liu R L, Xu Y, Zhu X, Zhao Y. J. Phys. Chem., 2021, 125(39): 21352.
[58]
Gong S, Wang S, Zhu T S, Chen X, Yang Z Z, Buehler M J, Shao-Horn Y, Grossman J C. JACS Au., 2021, 1(11): 1904.

doi: 10.1021/jacsau.1c00260     URL    
[59]
Zhang H K, Wang Z L, Ren J H, Liu J Y, Li J J. Energy Storage Mater., 2021, 35: 88.
[60]
Song D X, Chen X, Lin Z Z, Tang Z L, Ma W G, Zhang Q, Li Y S, Zhang X. ACS Nano, 2021, 15(10): 16469.

doi: 10.1021/acsnano.1c05920     URL    
[61]
Ai Y, Lu Z, Wei X, Zhang R. J Inorg Mater., 2021, 36(11): 1178-1184.

doi: 10.15541/jim20200748     URL    
[62]
Chen L, Huang S B, Qiu J Y, Zhang H, Cao G P. Prog. Chem., 2021, 33(8): 1378.
陈龙, 黄少博, 邱景义, 张浩, 曹高萍. 化学进展. 2021, 33(8): 1378.).

doi: 10.7536/PC200734    
[63]
Lu J S, Chen J M, He T X, Zhao J W, Liu J, Huo Y P. Prog. Chem., 2021, 33(8): 1344.
陆嘉晟, 陈嘉苗, 何天贤, 赵经纬, 刘军, 霍延平. 化学进展. 2021, 33(8): 1344.).

doi: 10.7536/PC200772    
[64]
Xue Z G, He D, Xie X L. J. Mater. Chem., 2015, 3(38): 19218.
[65]
Jalem R, Aoyama T, Nakayama M, Nogami M. Chem. Mater., 2012, 24(7): 1357.

doi: 10.1021/cm3000427     URL    
[66]
Zhang Y, Xu X J, Ind. Eng. Chem. Res., 2021, 60(1): 343.

doi: 10.1021/acs.iecr.0c05055     URL    
[67]
Choi E, Jo J, Kim W, Min K. ACS Appl. Mater. Interfaces., 2021, 13(36): 42590.

doi: 10.1021/acsami.1c07999     URL    
[68]
Wang C H, Aoyagi K, Aykol M, Mueller T. ACS Appl. Mater. Interfaces., 2020, 12(49): 55510.

doi: 10.1021/acsami.0c17285     URL    
[69]
Sendek A D, Cheon G, Pasta M, Reed E J. J. Phys. Chem., 2020, 124(15): 8067.
[70]
Li F, Cheng X B, Lu L L, Yin Y C, Luo J D, Lu G X, Meng Y F, Mo H S, Tian T, Yang J T, Wen W, Liu Z P, Zhang G Z, Shang C, Yao H B. Nano Lett., 2022, 22(6): 2461.

doi: 10.1021/acs.nanolett.2c00187     URL    
[71]
Chen Y T, Duquesnoy M, Tan D H S, Doux J M, Yang H D, Deysher G, Ridley P, Franco A A, Meng Y S, Chen Z. ACS Energy Lett., 2021, 6(4): 1639.
[72]
Jo J, Choi E, Kim M, Min K. ACS Appl. Energy Mater., 2021, 4(8): 7862-7869.

doi: 10.1021/acsaem.1c01223     URL    
[73]
Tang X P, Yao K, Liu B Y, Hu W G, Gao F R. Energies., 2018, 11(1): 86.

doi: 10.3390/en11010086     URL    
[74]
Ali S, Glass T, Parr B, Potgieter J, Alam F. IEEE Trans. Instrum. Meas., 2021, 70: 5500511.
[75]
Shodiev A, Duquesnoy M, Arcelus O, Chouchane M, Li J L, Franco A A. J. Power Sources, 2021, 511: 230384.

doi: 10.1016/j.jpowsour.2021.230384     URL    
[76]
Zahid T, Xu K, Li W M. Electron. Lett., 2017, 53(25): 1665.

doi: 10.1049/ell2.v53.25     URL    
[77]
Yang R X, Xiong R, Shen W X, Lin X F. Engineering., 2021, 7(3): 395.
[78]
Huang G Y, Huang X, Du J W, Sun X H, Li B T, Ye H M. Prog. Chem., 2021, 33(5): 855.
黄国勇, 董曦, 杜建委, 孙晓华, 李勃天, 叶海木. 化学进展. 2021, 33(5): 855.).

doi: 10.7536/PC200634    
[79]
Ellis L D, Buteau S, Hames S G, Thompson L M, Hall D S, Dahn J R. J. Electrochem. Soc., 2018, 165(2): A256.

doi: 10.1149/2.0861802jes     URL    
[80]
Scherer C, Scheid R, Andrienko D, Bereau T. J. Chem. Theory Comput., 2020, 16(5): 3194.

doi: 10.1021/acs.jctc.9b01256     URL    
[81]
Nakhaei-Kohani R, Ali Madani S, Mousavi S P, Atashrouz S, Abedi A, Hemmati-Sarapardeh A, Mohaddespour A. J. Mol. Liq., 2022, 362: 119509.

doi: 10.1016/j.molliq.2022.119509     URL    
[82]
Xiao R J, Li H, Chen L Q. Acta Phys. Sin., 2018, 67(12): 128801.

doi: 10.7498/aps     URL    
[83]
Sun Y W, Yang T Z, Ji H Q, Zhou J Q, Wang Z K, Qian T, Yan C L. Adv. Energy Mater., 2020, 10(41): 2002373.

doi: 10.1002/aenm.v10.41     URL    
[84]
Baktash A, Reid J C, Yuan Q H, Roman T, Searles D J. Adv. Mater., 2020, 32(18): 1908041.

doi: 10.1002/adma.v32.18     URL    
[85]
Sendek A D, Cubuk E D, Antoniuk E R, Cheon G, Cui Y, Reed E J. Chem. Mater., 2019, 31(2): 342.

doi: 10.1021/acs.chemmater.8b03272     URL    
[86]
Lin M, Liu X S, Xiang Y X, Wang F, Liu Y P, Fu R Q, Cheng J, Yang Y. Angew. Chem. Int. Ed., 2021, 60(22): 12547.

doi: 10.1002/anie.v60.22     URL    
[87]
Chmiela S, Sauceda H E, Müller K R, Tkatchenko A. Nat. Commun., 2018, 9: 3887.

doi: 10.1038/s41467-018-06169-2     pmid: 30250077
[88]
Unke O T, Chmiela S, Sauceda H E, Gastegger M, Poltavsky I, Schütt K T, Tkatchenko A, Müller K R. Chem. Rev., 2021, 121(16): 10142.

doi: 10.1021/acs.chemrev.0c01111     URL    
[89]
Chan H, Narayanan B, Cherukara M J, Sen F G, Sasikumar K, Gray S K, Chan M K Y, Sankaranarayanan S K R S. J. Phys. Chem., 2019, 123(12): 6941.
[90]
Sui X, He S, Huang X R, Teodorescu R, Stroe D I, IEEE. Iecon., 2020, 4(9): 1779.
[91]
NoÉ F, Tkatchenko A, Müller K R, Clementi C. Annu. Rev. Phys. Chem., 2020, 71: 361.

doi: 10.1146/physchem.2020.71.issue-1     URL    
[92]
Eremin R A, Zolotarev P N, Ivanshina O Y, Bobrikov I A. J. Phys. Chem., 2017, 121(51): 28293.
[93]
Fujimura K, Seko A, Koyama Y, Kuwabara A, Kishida I, Shitara K, Fisher C A J, Moriwake H, Tanaka I. Adv. Energy Mater., 2013, 3(8): 980.

doi: 10.1002/aenm.v3.8     URL    
[94]
Callaghan S. Patterns., 2021, 2(3): 100221.

doi: 10.1016/j.patter.2021.100221     URL    
[95]
Benayad A, Diddens D, Heuer A, Krishnamoorthy A N, Maiti M, Le Cras F, Legallais M, Rahmanian F, Shin Y, Stein H, Winter M, Wölke C, Yan P, Cekic-Laskovic I. Adv. Energy Mater., 2022, 12(17): 2102678.

doi: 10.1002/aenm.v12.17     URL    
[96]
Kafle J, Harris J, Chang J, Koshina J, Boone D, Qu D Y. J. Power Sources., 2018, 392: 60.

doi: 10.1016/j.jpowsour.2018.04.102     URL    
[97]
Whitacre J F, Mitchell J, Dave A, Wu W, Burke S, Viswanathan V. J. Electrochem. Soc., 2019, 166(16): A4181.

doi: 10.1149/2.0521916jes    
[1] 岳昕阳, 包戬, 马萃, 吴晓京, 周永宁. 热熔灌输法制备三维骨架支撑金属锂复合负极[J]. 化学进展, 2022, 34(3): 683-695.
[2] 李炜, 梁添贵, 林元创, 吴伟雄, 李松. 机器学习辅助高通量筛选金属有机骨架材料[J]. 化学进展, 2022, 34(12): 2619-2637.
[3] 陈龙, 黄少博, 邱景义, 张浩, 曹高萍. 聚合物固态锂电池电解质/负极界面[J]. 化学进展, 2021, 33(8): 1378-1389.
[4] 杨琪, 邓南平, 程博闻, 康卫民. 锂电池中的凝胶聚合物电解质[J]. 化学进展, 2021, 33(12): 2270-2282.
[5] 刘秋艳, 王雪锋, 王兆翔, 陈立泉. 高陶瓷含量复合固态电解质[J]. 化学进展, 2021, 33(1): 124-135.
[6] 陈嘉苗, 熊靖雯, 籍少敏, 霍延平, 赵经纬, 梁亮. 锂电池用全固态聚合物电解质[J]. 化学进展, 2020, 32(4): 481-496.
[7] 程新兵, 张强*. 金属锂枝晶生长机制及抑制方法[J]. 化学进展, 2018, 30(1): 51-72.
[8] 张恒, 郑丽萍, 聂进, 黄学杰, 周志彬. 锂单离子导电固态聚合物电解质[J]. 化学进展, 2014, 26(06): 1005-1020.
[9] 冯旭,何向明,蒲薇华,万春荣,姜长印. 锂电池正极材料FeS2[J]. 化学进展, 2008, 20(0203): 396-404.
[10] 程琥 李涛 杨勇 . 锂/聚合物电解质电化学固/固界面的研究[J]. 化学进展, 2006, 18(05): 542-549.
[11] 赵峰,钱新明,汪尔康,董绍俊. 离子导电聚合物电解质的研究*[J]. 化学进展, 2002, 14(05): 374-.