Special Topic: AI for Chemistry
Open Access
Natl Sci Open
Volume 3, Number 2, 2024
Special Topic: AI for Chemistry
Article Number 20230039
Number of page(s) 24
Section Chemistry
DOI https://doi.org/10.1360/nso/20230039
Published online 19 December 2023
  • Burrows HD, Hartshorn RM, Weir RD. The 2019 nobel prize in chemistry. Pure Appl Chem 2019; 91: 1717. [Article] [Google Scholar]
  • Goodenough JB, Park KS. The Li-ion rechargeable battery: A perspective. J Am Chem Soc 2013; 135: 1167-1176. [Article] [Google Scholar]
  • Kamat PV. Lithium-ion batteries and beyond: Celebrating the 2019 nobel prize in chemistry—a virtual issue. ACS Energy Lett 2019; 4: 2757-2759. [Article] [Google Scholar]
  • Manthiram A. A reflection on lithium-ion battery cathode chemistry. Nat Commun 2020; 11: 1550. [Article] [Google Scholar]
  • Evarts EC. Lithium batteries: To the limits of lithium. Nature 2015; 526: S93-S95. [Article] [Google Scholar]
  • Winter M, Barnett B, Xu K. Before Li ion batteries. Chem Rev 2018; 118: 11433-11456. [Article] [Google Scholar]
  • Wang H, Yu Z, Kong X, et al. Liquid electrolyte: The nexus of practical lithium metal batteries. Joule 2022; 6: 588-616. [Article] [Google Scholar]
  • Zheng J, Zhang W, Huang C, et al. In-situ polymerization with dual-function electrolyte additive toward future lithium metal batteries. Mater Today Energy 2022; 26: 100984. [Article] [Google Scholar]
  • Jia M, Zhang C, Guo Y, et al. Advanced nonflammable localized high-concentration electrolyte for high energy density lithium battery. Energy Environ Mater 2022; 5: 1294-1302. [Article] [Google Scholar]
  • Wang R, Cui W, Chu F, et al. Lithium metal anodes: Present and future. J Energy Chem 2020; 48: 145-159. [Article] [Google Scholar]
  • Zhang JG, Xu W, Xiao J, et al. Lithium metal anodes with nonaqueous electrolytes. Chem Rev 2020; 120: 13312-13348. [Article] [Google Scholar]
  • Hobold GM, Lopez J, Guo R, et al. Moving beyond 99.9% Coulombic efficiency for lithium anodes in liquid electrolytes. Nat Energy 2021; 6: 951-960. [Article] [Google Scholar]
  • Cheng XB, Zhang R, Zhao CZ, et al. Toward safe lithium metal anode in rechargeable batteries: A review. Chem Rev 2017; 117: 10403-10473. [Article] [Google Scholar]
  • Kim S, Park G, Lee SJ, et al. Lithium-metal batteries: From fundamental research to industrialization. Adv Mater 2023; 35: 2206625. [Article] [Google Scholar]
  • Wang J, Yamada Y, Sodeyama K, et al. Superconcentrated electrolytes for a high-voltage lithium-ion battery. Nat Commun 2016; 7: 12032. [Article] [Google Scholar]
  • Ren X, Zou L, Jiao S, et al. High-concentration ether electrolytes for stable high-voltage lithium metal batteries. ACS Energy Lett 2019; 4: 896-902. [Article] [Google Scholar]
  • Ren X, Chen S, Lee H, et al. Localized high-concentration sulfone electrolytes for high-efficiency lithium-metal batteries. Chem 2018; 4: 1877-1892. [Article] [Google Scholar]
  • Chen S, Zheng J, Mei D, et al. High-voltage lithium-metal batteries enabled by localized high-concentration electrolytes. Adv Mater 2018; 30: 1706102. [Article] [Google Scholar]
  • Cao X, Jia H, Xu W, et al. Review—Localized high-concentration electrolytes for lithium batteries. J Electrochem Soc 2021; 168: 010522. [Article] [Google Scholar]
  • Yu Z, Wang H, Kong X, et al. Molecular design for electrolyte solvents enabling energy-dense and long-cycling lithium metal batteries. Nat Energy 2020; 5: 526-533. [Article] [Google Scholar]
  • Yu Z, Rudnicki PE, Zhang Z, et al. Rational solvent molecule tuning for high-performance lithium metal battery electrolytes. Nat Energy 2022; 7: 94-106. [Article] [Google Scholar]
  • Zhao Y, Zhou T, Ashirov T, et al. Fluorinated ether electrolyte with controlled solvation structure for high voltage lithium metal batteries. Nat Commun 2022; 13: 2575. [Article] [Google Scholar]
  • Xie J, Sun S, Chen X, et al. Fluorinating the solid electrolyte interphase by rational molecular design for practical lithium-metal batteries. Angew Chem Int Ed 2022; 61: e202204776. [Article] [Google Scholar]
  • Pham TD, Bin Faheem A, Kim J, et al. Practical high-voltage lithium metal batteries enabled by tuning the solvation structure in weakly solvating electrolyte. Small 2022; 18: 2107492. [Article] [Google Scholar]
  • Zhang H, Zeng Z, Ma F, et al. Cyclopentylmethyl ether, a non-fluorinated, weakly solvating and wide temperature solvent for high-performance lithium metal battery. Angew Chem Int Ed 2023; 62: e202300771. [Article] [Google Scholar]
  • Li W, Yao H, Yan K, et al. The synergetic effect of lithium polysulfide and lithium nitrate to prevent lithium dendrite growth. Nat Commun 2015; 6: 7436. [Article] [Google Scholar]
  • Zheng J, Engelhard MH, Mei D, et al. Electrolyte additive enabled fast charging and stable cycling lithium metal batteries. Nat Energy 2017; 2: 17012. [Article] [Google Scholar]
  • Yan C, Yao Y, Chen X, et al. Lithium nitrate solvation chemistry in carbonate electrolyte sustains high-voltage lithium metal batteries. Angew Chem Int Ed 2018; 57: 14055-14059. [Article] [Google Scholar]
  • Li F, He J, Liu J, et al. Gradient solid electrolyte interphase and lithium-ion solvation regulated by bisfluoroacetamide for stable lithium metal batteries. Angew Chem Int Ed 2021; 60: 6600-6608. [Article] [Google Scholar]
  • Wang Q, Zhao C, Wang J, et al. High entropy liquid electrolytes for lithium batteries. Nat Commun 2023; 14: 440. [Article] [Google Scholar]
  • Wang Q, Zhao C, Yao Z, et al. Entropy-driven liquid electrolytes for lithium batteries. Adv Mater 2023; 35: 2210677. [Article] [Google Scholar]
  • Jiao S, Ren X, Cao R, et al. Stable cycling of high-voltage lithium metal batteries in ether electrolytes. Nat Energy 2018; 3: 739-746. [Article] [Google Scholar]
  • Liu J, Zhang Y, Zhou J, et al. Advances and prospects in improving the utilization efficiency of lithium for high energy density lithium batteries. Adv Funct Mater 2023; 33: 2302055. [Article] [Google Scholar]
  • Chen Y, Li M, Liu Y, et al. Origin of dendrite-free lithium deposition in concentrated electrolytes. Nat Commun 2023; 14: 2655. [Article] [Google Scholar]
  • Jie Y, Xu Y, Chen Y, et al. Molecular understanding of interphase formation via operando polymerization on lithium metal anode. Cell Rep Phys Sci 2022; 3: 101057. [Article] [Google Scholar]
  • Zhang Z, Li Y, Xu R, et al. Capturing the swelling of solid-electrolyte interphase in lithium metal batteries. Science 2022; 375: 66-70. [Article] [Google Scholar]
  • Yao N, Chen X, Fu ZH, et al. Applying classical, ab initio, and machine-learning molecular dynamics simulations to the liquid electrolyte for rechargeable batteries. Chem Rev 2022; 122: 10970-11021. [Article] [Google Scholar]
  • Lombardo T, Duquesnoy M, El-Bouysidy H, et al. Artificial intelligence applied to battery research: Hype or reality?. Chem Rev 2022; 122: 10899-10969. [Article] [Google Scholar]
  • Clark S, Bleken FL, Stier S, et al. Toward a unified description of battery data. Adv Energy Mater 2022; 12: 2102702. [Article] [Google Scholar]
  • Aykol M, Herring P, Anapolsky A. Machine learning for continuous innovation in battery technologies. Nat Rev Mater 2020; 5: 725-727. [Article] [Google Scholar]
  • Yao Z, Lum Y, Johnston A, et al. Machine learning for a sustainable energy future. Nat Rev Mater 2023; 8: 202-215. [Article] arxiv:2210.10391 [Google Scholar]
  • Dave A, Mitchell J, Kandasamy K, et al. Autonomous discovery of battery electrolytes with robotic experimentation and machine learning. Cell Rep Phys Sci 2020; 1: 100264. [Article] [Google Scholar]
  • Kim SC, Oyakhire ST, Athanitis C, et al. Data-driven electrolyte design for lithium metal anodes. Proc Natl Acad Sci USA 2023; 120: e2214357120. [Article] [Google Scholar]
  • Temiz S, Kurban H, Erol S, et al. Regeneration of lithium-ion battery impedance using a novel machine learning framework and minimal empirical data. J Energy Storage 2022; 52: 105022. [Article] [Google Scholar]
  • Rahmanian F, Vogler M, Wölke C, et al. Conductivity experiments for electrolyte formulations and their automated analysis. Sci Data 2023; 10: 43. [Article] [Google Scholar]
  • Xiao Z, Yuan R, Zhao T, et al. Advances and applications of computational simulations in the inhibition of lithium dendrite growth. Ionics 2023; 29: 879-893. [Article] [Google Scholar]
  • Sun Q, Xiang Y, Liu Y, et al. Machine learning predicts the X-ray photoelectron spectroscopy of the solid electrolyte interface of lithium metal battery. J Phys Chem Lett 2022; 13: 8047-8054. [Article] [Google Scholar]
  • Niri MF, Apachitei G, Lain M, et al. Machine learning for investigating the relative importance of electrodes’ N:P areal capacity ratio in the manufacturing of lithium-ion battery cells. J Power Sources 2022; 549: 232124. [Article] [Google Scholar]
  • Diddens D, Appiah WA, Mabrouk Y, et al. Modeling the solid electrolyte interphase: Machine learning as a game changer?. Adv Mater Inter 2022; 9: 2101734. [Article] [Google Scholar]
  • Cheng D, Sha W, Wang L, et al. Solid-state lithium battery cycle life prediction using machine learning. Appl Sci 2021; 11: 4671. [Article] [Google Scholar]
  • Paulson NH, Kubal J, Ward L, et al. Feature engineering for machine learning enabled early prediction of battery lifetime. J Power Sources 2022; 527: 231127. [Article] [Google Scholar]
  • Liu Y, Guo B, Zou X, et al. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater 2020; 31: 434-450. [Article] [Google Scholar]
  • Lv C, Zhou X, Zhong L, et al. Machine learning: An advanced platform for materials development and state prediction in lithium-ion batteries. Adv Mater 2022; 34: 2101474. [Article] [Google Scholar]
  • Tu Z, Shi S, Zou X, et al. Applying data-driven machine learning to studying electrochemical energy storage materials. Energy Storage Sci Technol 2022; 11: 739-759 [Google Scholar]
  • Wei Z, He Q, Zhao Y. Machine learning for battery research. J Power Sources 2022; 549: 232125. [Article] [Google Scholar]
  • Liu Y, Yang Z, Zou X, et al. Data quantity governance for machine learning in materials science. Natl Sci Rev 2023; 10: nwad125. [Article] [Google Scholar]
  • Liu Y, Zhao T, Ju W, et al. Materials discovery and design using machine learning. J Materiomics 2017; 3: 159-177. [Article] [Google Scholar]
  • Liu Y, Yang Z, Yu Z, et al. Generative artificial intelligence and its applications in materials science: Current situation and future perspectives. J Materiomics 2023; 9: 798-816. [Article] [Google Scholar]
  • Villordon A, Clark C, Smith T, et al. Combining linear regression and machine learning approaches to identify consensus variables related to optimum sweetpotato transplanting date. HortScience 2010; 45: 684-686. [Article] [Google Scholar]
  • Tibshirani R. Regression shrinkage and selection via the lasso. J R Statistical Soc-Ser B (Methodological) 1996; 58: 267-288. [Article] [Google Scholar]
  • Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970; 12: 55-67. [Article] [Google Scholar]
  • Friedl MA, Brodley CE. Decision tree classification of land cover from remotely sensed data. Remote Sens Environ 1997; 61: 399-409. [Article] [Google Scholar]
  • Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Machine Intell 1998; 20: 832-844. [Article] [Google Scholar]
  • Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. Pittsburgh, 1992. 144-152 [Google Scholar]
  • Fonseca DJ, Navaresse DO, Moynihan GP. Simulation metamodeling through artificial neural networks. Eng Appl Artif Intelligence 2003; 16: 177-183. [Article] [Google Scholar]
  • Fung V, Hu G, Ganesh P, et al. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun 2021; 12: 88. [Article] [Google Scholar]
  • Ouyang R, Curtarolo S, Ahmetcik E, et al. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys Rev Mater 2018; 2: 083802. [Article] [Google Scholar]
  • Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Statist 2001; 29: 1189. [Article] [Google Scholar]
  • Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, 2016. 785-794 [Google Scholar]
  • Murtagh F, Contreras P. Algorithms for hierarchical clustering: An overview. WIREs Data Min Knowl 2011; 2: 86-97. [Article] [Google Scholar]
  • Abdi H, Williams LJ. Principal component analysis. WIREs Comput Stats 2010; 2: 433-459. [Article] [Google Scholar]
  • Balakrishnama S, Ganapathiraju A. Linear discriminant analysis-a brief tutorial. Inst Signal Inf Process 1998; 18: 1-8 [Google Scholar]
  • Schölkopf B, Smola A, Müller KR. Kernel principal component analysis. In: Gerstner W, Germond A, Hasler M, et al. (eds). Artificial Neural Networks—ICANN’97. ICANN 1997. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 1997 [Google Scholar]
  • Tenenbaum J. Mapping a manifold of perceptual observations. In: Proceedings of the 10th International Conference on Neural Information Processing Systems. Cambridge, 1997; 682-688 [Google Scholar]
  • Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science 2000; 290: 2323-2326. [Article] [Google Scholar]
  • van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res 2008; 9: 2579-2605 [Google Scholar]
  • He Z, Chen Y, Huang F, et al. Fluorinated solvents for lithium metal batteries. Acta Phys Chim Sin 2022; 38: 2205005. [Article] [Google Scholar]
  • Wang Q, Xu X, Hong B, et al. Molecular reactivity and interface stability modification in in-situ gel electrolyte for high performance quasi-solid-state lithium metal batteries. Energy Environ Mater 2022; 6: e12351. [Article] [Google Scholar]
  • Li M, Wang C, Davey K, et al. Recent progress in electrolyte design for advanced lithium metal batteries. SmartMat 2023; 4: e1185. [Article] [Google Scholar]
  • Wu M, Li Y, Liu X, et al. Perspective on solid-electrolyte interphase regulation for lithium metal batteries. SmartMat 2020; 2: 5-11. [Article] [Google Scholar]
  • Yao N, Chen X, Shen X, et al. An atomic insight into the chemical origin and variation of the dielectric constant in liquid electrolytes. Angew Chem Int Ed 2021; 60: 21473-21478. [Article] [Google Scholar]
  • Wu Y, Hu Q, Liang H, et al. Electrostatic potential as solvent descriptor to enable rational electrolyte design for lithium batteries. Adv Energy Mater 2023; 13: 2300259. [Article] [Google Scholar]
  • Ko S, Obukata T, Shimada T, et al. Electrode potential influences the reversibility of lithium-metal anodes. Nat Energy 2022; 7: 1217-1224. [Article] [Google Scholar]
  • Wang F, Cheng J. Understanding the solvation structures of glyme-based electrolytes by machine learning molecular dynamics. Chin J Struct Chem 2023; 42: 100061. [Article] [Google Scholar]
  • Wang AA, Greenbank S, Li G, et al. Current-driven solvent segregation in lithium-ion electrolytes. Cell Rep Phys Sci 2022; 3: 101047. [Article] [Google Scholar]
  • Liu Y, Yu P, Wu Y, et al. The DFT-ReaxFF hybrid reactive dynamics method with application to the reductive decomposition reaction of the TFSI and DOL electrolyte at a lithium-metal anode surface. J Phys Chem Lett 2021; 12: 1300-1306. [Article] [Google Scholar]
  • Wang F, Cheng J. Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning. Chem Sci 2022; 13: 11570-11576. [Article] [Google Scholar]
  • Blumberger J, Tavernelli I, Klein ML, et al. Diabatic free energy curves and coordination fluctuations for the aqueous Ag+∕Ag2+ redox couple: A biased Born-Oppenheimer molecular dynamics investigation. J Chem Phys 2006; 124: 064507. [Article] [Google Scholar]
  • Xie X, Clark Spotte-Smith EW, Wen M, et al. Data-driven prediction of formation mechanisms of lithium ethylene monocarbonate with an automated reaction network. J Am Chem Soc 2021; 143: 13245-13258. [Article] [Google Scholar]
  • Spotte-Smith EWC, Kam RL, Barter D, et al. Toward a mechanistic model of solid-electrolyte interphase formation and evolution in lithium-ion batteries. ACS Energy Lett 2022; 7: 1446-1453. [Article] [Google Scholar]
  • Nanda J, Yang G, Hou T, et al. Unraveling the nanoscale heterogeneity of solid electrolyte interphase using tip-enhanced raman spectroscopy. Joule 2019; 3: 2001-2019. [Article] [Google Scholar]
  • Li Y, Li Y, Pei A, et al. Atomic structure of sensitive battery materials and interfaces revealed by cryo-electron microscopy. Science 2017; 358: 506-510. [Article] [Google Scholar]
  • Wood KN, Steirer KX, Hafner SE, et al. Operando X-ray photoelectron spectroscopy of solid electrolyte interphase formation and evolution in Li2S-P2S5 solid-state electrolytes. Nat Commun 2018; 9: 2490. [Article] [Google Scholar]
  • Lang S, Colletta M, Krumov MR, et al. Multidimensional visualization of the dynamic evolution of Li metal via in situ/operando methods. Proc Natl Acad Sci USA 2023; 120: e2220419120. [Article] [Google Scholar]
  • Shadike Z, Lee H, Borodin O, et al. Identification of LiH and nanocrystalline LiF in the solid-electrolyte interphase of lithium metal anodes. Nat Nanotechnol 2021; 16: 549-554. [Article] [Google Scholar]
  • Zhou Y, Su M, Yu X, et al. Real-time mass spectrometric characterization of the solid-electrolyte interphase of a lithium-ion battery. Nat Nanotechnol 2020; 15: 224-230. [Article] [Google Scholar]
  • Feng G, Jia H, Shi Y, et al. Imaging solid-electrolyte interphase dynamics using operando reflection interference microscopy. Nat Nanotechnol 2023; 18: 780-789. [Article] [Google Scholar]
  • Wang L, Menakath A, Han F, et al. Identifying the components of the solid-electrolyte interphase in Li-ion batteries. Nat Chem 2019; 11: 789-796. [Article] [Google Scholar]
  • Gaberšček M. Understanding Li-based battery materials via electrochemical impedance spectroscopy. Nat Commun 2021; 12: 6513. [Article] [Google Scholar]
  • Zhang Y, Tang Q, Zhang Y, et al. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nat Commun 2020; 11: 1706. [Article] [Google Scholar]
  • Xiong R, Tian J, Shen W, et al. Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy. J Energy Chem 2023; 76: 404-413. [Article] [Google Scholar]
  • Guo J, Che Y, Pedersen K, et al. Battery impedance spectrum prediction from partial charging voltage curve by machine learning. J Energy Chem 2023; 79: 211-221. [Article] [Google Scholar]
  • Lai G, Jiao J, Fang C, et al. The mechanism of Li deposition on the Cu substrates in the anode-free Li metal batteries. Small 2023; 19: 2205416. [Article] [Google Scholar]
  • Zhang W, Weng M, Zhang M, et al. Revealing morphology evolution of lithium dendrites by large-scale simulation based on machine learning force field. Adv Energy Mater 2022; 13: 2202892. [Article] [Google Scholar]
  • Lai G, Zuo Y, Jiao J, et al. The mechanism of external pressure suppressing dendrites growth in Li metal batteries. J Energy Chem 2023; 79: 489-494. [Article] [Google Scholar]
  • Schütt KT, Kindermans PJ, Sauceda HE, et al. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, 2017. 992-1002 [Google Scholar]
  • Mercado R, Rastemo T, Lindelöf E, et al. Graph networks for molecular design. Mach Learn-Sci Technol 2021; 2: 025023. [Article] [Google Scholar]
  • Mercado R, Rastemo T, Lindelöf E, et al. Practical notes on building molecular graph generative models. Appl AI Lett 2020; 1: ail2.18. [Article] [Google Scholar]
  • Zhou G, Gao Z, Ding Q, et al. Uni-mol: A universal 3D molecular representation learning framework. ChemRxiv, 2023; [Article] [Google Scholar]
  • Ahmad Z, Xie T, Maheshwari C, et al. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent Sci 2018; 4: 996-1006. [Article] [Google Scholar]
  • Ren Y, Zhang K, Zhou Y, et al. Phase-field simulation and machine learning study of the effects of elastic and plastic properties of electrodes and solid polymer electrolytes on the suppression of Li dendrite growth. ACS Appl Mater Interfaces 2022; 14: 30658-30671. [Article] [Google Scholar]
  • Li Y, Liu K, Foley AM, et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renew Sustain Energy Rev 2019; 113: 109254. [Article] [Google Scholar]
  • Thelen A, Lui YH, Shen S, et al. Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries. Energy Storage Mater 2022; 50: 668-695. [Article] [Google Scholar]
  • Severson KA, Attia PM, Jin N, et al. Data-driven prediction of battery cycle life before capacity degradation. Nat Energy 2019; 4: 383-391. [Article] [Google Scholar]
  • Jiang B, Zhu J, Wang X, et al. A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries. Appl Energy 2022; 322: 119502. [Article] [Google Scholar]
  • Li W, Chen J, Quade K, et al. Battery degradation diagnosis with field data, impedance-based modeling and artificial intelligence. Energy Storage Mater 2022; 53: 391-403. [Article] [Google Scholar]
  • Liu X, Peng H, Li B, et al. Untangling degradation chemistries of lithium-sulfur batteries through interpretable hybrid machine learning. Angew Chem Int Ed 2022; 61: e202214037. [Article] [Google Scholar]
  • Harris SJ, Harris DJ, Li C. Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells. J Power Sources 2017; 342: 589-597. [Article] [Google Scholar]
  • Dong J, Yu Z, Zhang X, et al. Data-driven predictive prognostic model for power batteries based on machine learning. Proc Saf Environ Protect 2023; 172: 894-907. [Article] [Google Scholar]
  • Gong D, Gao Y, Kou Y, et al. Early prediction of cycle life for lithium-ion batteries based on evolutionary computation and machine learning. J Energy Storage 2022; 51: 104376. [Article] [Google Scholar]
  • Mansouri SS, Karvelis P, Georgoulas G, et al. Remaining useful battery life prediction for UAVs based on machine learning. IFAC-PapersOnLine 2017; 50: 4727-4732. [Article] [Google Scholar]
  • Jin S, Sui X, Huang X, et al. Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction. Electronics 2021; 10: 3126. [Article] [Google Scholar]

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