Issue

Vol. 17 (2025)

Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation

https://doi.org/10.1007/s40820-025-01797-y
Sheng Wang
School of Future Science and Engineering, Soochow University, Suzhou 215222, People’s Republic of China
Jincheng Liu
School of Future Science and Engineering, Soochow University, Suzhou 215222, People’s Republic of China
Xiaopan Song
School of Electronics Science and Engineering, Nanjing University, Nanjing 210023, People’s Republic of China
Huajian Xu
School of Electronics Science and Engineering, Soochow University, Suzhou 215006, People’s Republic of China
Yang Gu
School of Future Science and Engineering, Soochow University, Suzhou 215222, People’s Republic of China
Junyu Fan
School of Future Science and Engineering, Soochow University, Suzhou 215222, People’s Republic of China
Bin Sun
School of Future Science and Engineering, Soochow University, Suzhou 215222, People’s Republic of China
Linwei Yu
School of Electronics Science and Engineering, Nanjing University, Nanjing 210023, People’s Republic of China

Corresponding Author: Linwei Yu

yulinwei@nju.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 287

Abstract

Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy, high safety, and high environmental adaptability. However, the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment, rendering performance prediction arduous and delaying large-scale industrialization. Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction. This review will systematically examine how the latest progress in using machine learning (ML) algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode, anode, and electrolyte materials suitable for solid-state batteries. Furthermore, the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed, among which are state of charge, state of health, remaining useful life, and battery capacity. Finally, we will summarize the main challenges encountered in the current research, such as data quality issues and poor code portability, and propose possible solutions and development paths. These will provide clear guidance for future research and technological reiteration.


Highlights:
1 The latest advancements in the application of machine learning (ML) for the screening of solid-state battery materials are reviewed.
2 The achievements of various ML algorithms in predicting different performances of the battery management system are discussed.
3 Future challenges and perspectives of artificial intelligence in solid-state battery are discussed.

Keywords

Solid-state batteries Artificial intelligence Deep learning Material screening Performance evaluation
Wang, Sheng, Jincheng Liu, Xiaopan Song, Huajian Xu, Yang Gu, Junyu Fan, Bin Sun, and Linwei Yu. 2025. “Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation”. Nano-Micro Letters 17 (June):287. https://doi.org/10.1007/s40820-025-01797-y.
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References
  1. K.K. Jaiswal, C.R. Chowdhury, D. Yadav, R. Verma, S. Dutta et al., Renewable and sustainable clean energy development and impact on social, economic, and environmental health. Energy Nexus 7, 100118 (2022). https://doi.org/10.1016/j.nexus.2022.100118
  2. P. Sorknæs, H. Lund, A.N. Andersen, Future power market and sustainable energy solutions–The treatment of uncertainties in the daily operation of combined heat and power plants. Appl. Energy 144, 129–138 (2015). https://doi.org/10.1016/j.apenergy.2015.02.041
  3. K. Jana, A. Ray, M.M. Majoumerd, M. Assadi, S. De, Polygeneration as a future sustainable energy solution–A comprehensive review. Appl. Energy 202, 88–111 (2017). https://doi.org/10.1016/j.apenergy.2017.05.129
  4. J.A. Sanguesa, V. Torres-Sanz, P. Garrido, F.J. Martinez, J.M. Marquez-Barja, A review on electric vehicles: technologies and challenges. Smart Cities 4(1), 372–404 (2021). https://doi.org/10.3390/smartcities4010022
  5. M.S. Hossain, L. Kumar, M. El Haj Assad, R. Alayi, Advancements and future prospects of electric vehicle technologies: a comprehensive review. Complexity 2022(1), 3304796 (2022). https://doi.org/10.1155/2022/3304796
  6. S.P. Sathiyan, C.B. Pratap, A.A. Stonier, G. Peter, A. Sherine et al., Comprehensive assessment of electric vehicle development, deployment, and policy initiatives to reduce GHG emissions: opportunities and challenges. IEEE Access 10, 53614–53639 (2022). https://doi.org/10.1109/ACCESS.2022.3175585
  7. D. Li, D. Yu, G. Zhang, High configuration entropy promises electrochemical stability of chloride electrolytes for high-energy, long-life all-solid-state batteries. Angew. Chem. Int. Ed. 137(7), e202419735 (2025). https://doi.org/10.1002/ange.202419735
  8. M.J. Kim, J.S. Park, J.W. Lee, S.E. Wang, D. Yoon et al., Half-covered ‘glitter-cake’ AM@SE composite: a novel electrode design for high energy density all-solid-state batteries. Nano-Micro Lett. 17(1), 119 (2025). https://doi.org/10.1007/s40820-024-01644-6
  9. W.-M. Qin, Z. Li, W.-X. Su, J.-M. Hu, H. Zou et al., Porous organic cage-based quasi-solid-state electrolyte with cavity-induced anion-trapping effect for long-life lithium metal batteries. Nano-Micro Lett. 17(1), 38 (2024). https://doi.org/10.1007/s40820-024-01499-x
  10. B. Kim, M.-C. Sung, G.-H. Lee, B. Hwang, S. Seo et al., Aligned ion conduction pathway of polyrotaxane-based electrolyte with dispersed hydrophobic chains for solid-state lithium-oxygen batteries. Nano-Micro Lett. 17(1), 31 (2024). https://doi.org/10.1007/s40820-024-01535-w
  11. S. Wang, H. Song, T. Zhu, J. Chen, Z. Yu et al., An ultralow-charge-overpotential and long-cycle-life solid-state Li-CO2 battery enabled by plasmon-enhanced solar photothermal catalysis. Nano Energy 100, 107521 (2022). https://doi.org/10.1016/j.nanoen.2022.107521
  12. H. Xu, X. Song, Y. Gu, J. Fan, J. Liu et al., Failure mechanisms and design strategies for low-temperature solid-state metal batteries. J. Mater. Chem. A 13(15), 10388–10414 (2025). https://doi.org/10.1039/D4TA07644C
  13. M. Cukurova, The interplay of learning, analytics and artificial intelligence in education: a vision for hybrid intelligence. Br. J. Educ. Technol. 56(2), 469–488 (2025). https://doi.org/10.1111/bjet.13514
  14. B. Bhinder, C. Gilvary, N.S. Madhukar, O. Elemento, Artificial intelligence in cancer research and precision medicine. Cancer Discov. 11(4), 900–915 (2021). https://doi.org/10.1158/2159-8290.cd-21-0090
  15. Y. Guo, K. Li, W. Yue, N.Y. Kim, Y. Li et al., A rapid adaptation approach for dynamic air-writing recognition using wearable wristbands with self-supervised contrastive learning. Nano-Micro Lett 17(1), 41 (2024). https://doi.org/10.1007/s40820-024-01545-8
  16. W. Lyu, J. Liu, Artificial Intelligence and emerging digital technologies in the energy sector. Appl. Energy 303, 117615 (2021). https://doi.org/10.1016/j.apenergy.2021.117615
  17. T. Sun, B. Feng, J. Huo, Y. Xiao, W. Wang et al., Artificial intelligence meets flexible sensors: emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano-Micro Lett. 16(1), 14 (2023). https://doi.org/10.1007/s40820-023-01235-x
  18. H. Haick, N. Tang, Artificial intelligence in medical sensors for clinical decisions. ACS Nano 15(3), 3557–3567 (2021). https://doi.org/10.1021/acsnano.1c00085
  19. Y. Deng, T. Zhang, G. Lou, X. Zheng, J. Jin et al., Deep learning-based autonomous driving systems: a survey of attacks and defenses. IEEE Trans. Ind. Inf. 17(12), 7897–7912 (2021). https://doi.org/10.1109/tii.2021.3071405
  20. M.L. Smith, L.N. Smith, M.F. Hansen, The quiet revolution in machine visio—a state-of-the-art survey paper, including historical review, perspectives, and future directions. Comput. Ind. 130, 103472 (2021). https://doi.org/10.1016/j.compind.2021.103472
  21. M. Paramesha, N. Rane, J. Rane, Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Partn. Univ. Multidisc. Res. J. 1(2), 110–133 (2024). https://doi.org/10.2139/ssrn.4855856
  22. X. Zhang, S. Cheng, C. Fu, G. Yin, L. Wang et al., Advancements and challenges in organic-inorganic composite solid electrolytes for all-solid-state lithium batteries. Nano-Micro Lett. 17(1), 2 (2024). https://doi.org/10.1007/s40820-024-01498-y
  23. A. Machín, C. Morant, F. Márquez, Advancements and challenges in solid-state battery technology: an in-depth review of solid electrolytes and anode innovations. Batteries 10(1), 29 (2024). https://doi.org/10.3390/batteries10010029
  24. R. Chen, W. Qu, X. Guo, L. Li, F. Wu, The pursuit of solid-state electrolytes for lithium batteries: from comprehensive insight to emerging horizons. Mater. Horiz. 3(6), 487–516 (2016). https://doi.org/10.1039/C6MH00218H
  25. H. Guo, Q. Wang, A. Stuke, A. Urban, N. Artrith, Accelerated atomistic modeling of solid-state battery materials with machine learning. Front. Energy Res. 9, 695902 (2021). https://doi.org/10.3389/fenrg.2021.695902
  26. Q. Hu, K. Chen, F. Liu, M. Zhao, F. Liang et al., Smart materials prediction: applying machine learning to lithium solid-state electrolyte. Materials 15(3), 1157 (2022). https://doi.org/10.3390/ma15031157
  27. Z. Ahmad, T. Xie, C. Maheshwari, J.C. Grossman, V. Viswanathan, Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent. Sci. 4(8), 996–1006 (2018). https://doi.org/10.1021/acscentsci.8b00229
  28. A. Hajibabaei, C.W. Myung, K.S. Kim, Sparse Gaussian process potentials: Application to lithium diffusivity in superionic conducting solid electrolytes. Phys. Rev. B 103(21), 214102 (2021). https://doi.org/10.1103/physrevb.103.214102
  29. T. Zahid, K. Xu, W. Li, C. Li, H. Li, State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles. Energy 162, 871–882 (2018). https://doi.org/10.1016/j.energy.2018.08.071
  30. I.B. Espedal, A. Jinasena, O.S. Burheim, J.J. Lamb, Current trends for state-of-charge (SoC) estimation in lithium-ion battery electric vehicles. Energies 14(11), 3284 (2021). https://doi.org/10.3390/en14113284
  31. S. Yang, C. Zhang, J. Jiang, W. Zhang, L. Zhang et al., Review on state-of-health of lithium-ion batteries: characterizations, estimations and applications. J. Clean. Prod. 314, 128015 (2021). https://doi.org/10.1016/j.jclepro.2021.128015
  32. M.-F. Ge, Y. Liu, X. Jiang, J. Liu, A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries. Measurement 174, 109057 (2021). https://doi.org/10.1016/j.measurement.2021.109057
  33. Z. Tao, Z. Zhao, C. Wang, L. Huang, H. Jie et al., State of charge estimation of lithium batteries: review for equivalent circuit model methods. Measurement 236, 115148 (2024). https://doi.org/10.1016/j.measurement.2024.115148
  34. L. Barzacchi, M. Lagnoni, R. Di Rienzo, A. Bertei, F. Baronti, Enabling early detection of lithium-ion battery degradation by linking electrochemical properties to equivalent circuit model parameters. J. Energy Storage 50, 104213 (2022). https://doi.org/10.1016/j.est.2022.104213
  35. G.O. Sahinoglu, M. Pajovic, Z. Sahinoglu, Y. Wang, P.V. Orlik et al., Battery state-of-charge estimation based on regular/recurrent Gaussian process regression. IEEE Trans. Ind. Electron. 65(5), 4311–4321 (2018). https://doi.org/10.1109/tie.2017.2764869
  36. P. Thakkar, S. Khatri, D. Dobariya, D. Patel, B. Dey et al., Advances in materials and machine learning techniques for energy storage devices: a comprehensive review. J. Energy Storage 81, 110452 (2024). https://doi.org/10.1016/j.est.2024.110452
  37. S. Wang, S. Jin, D. Bai, Y. Fan, H. Shi et al., A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Rep. 7, 5562–5574 (2021). https://doi.org/10.1016/j.egyr.2021.08.182
  38. M.A. Hannan, M.S.H. Lipu, A. Hussain, A. Mohamed, A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew. Sustain. Energy Rev. 78, 834–854 (2017). https://doi.org/10.1016/j.rser.2017.05.001
  39. P. Dini, A. Colicelli, S. Saponara, Review on modeling and SOC/SOH estimation of batteries for automotive applications. Batteries 10(1), 34 (2024). https://doi.org/10.3390/batteries10010034
  40. Y. Qiu, X. Zhang, Y. Tian, Z. Zhou, Machine learning promotes the development of all-solid-state batteries. Chin. J. Struct. Chem. 42(9), 100118 (2023). https://doi.org/10.1016/j.cjsc.2023.100118
  41. J. Janek, W.G. Zeier, Challenges in speeding up solid-state battery development. Nat. Energy 8(3), 230–240 (2023). https://doi.org/10.1038/s41560-023-01208-9
  42. T. Xie, J.C. Grossman, Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120(14), 145301 (2018). https://doi.org/10.1103/physrevlett.120.145301
  43. M.-F. Ng, Y. Sun, Z.W. Seh, Machine learning-inspired battery material innovation. Energy Adv. 2(4), 449–464 (2023). https://doi.org/10.1039/D3YA00040K
  44. X. He, J. Peng, Q. Lin, M. Li, W. Chen et al., Sulfolane-based flame-retardant electrolyte for high-voltage sodium-ion batteries. Nano-Micro Lett 17(1), 45 (2024). https://doi.org/10.1007/s40820-024-01546-7
  45. X. Fan, C. Wang, High-voltage liquid electrolytes for Li batteries: progress and perspectives. Chem. Soc. Rev. 50(18), 10486–10566 (2021). https://doi.org/10.1039/D1CS00450F
  46. Q. Wang, C. Zhao, Z. Yao, J. Wang, F. Wu et al., Entropy-driven liquid electrolytes for lithium batteries. Adv. Mater. 35(17), 2210677 (2023). https://doi.org/10.1002/adma.202210677
  47. Z. Li, K.-S. Oh, J.-M. Seo, W. Qin, S. Lee et al., A solvent-free covalent organic framework single-ion conductor based on ion-dipole interaction for all-solid-state lithium organic batteries. Nano-Micro Lett. 16(1), 265 (2024). https://doi.org/10.1007/s40820-024-01485-3
  48. S. Wang, J. Wang, J. Liu, H. Song, Y. Liu et al., Ultra-fine surface solid-state electrolytes for long cycle life all-solid-state lithium–air batteries. J. Mater. Chem. A 6(43), 21248–21254 (2018). https://doi.org/10.1039/C8TA08095J
  49. Z. Gao, H. Sun, L. Fu, F. Ye, Y. Zhang et al., Promises, challenges, and recent progress of inorganic solid-state electrolytes for all-solid-state lithium batteries. Adv. Mater. 30(17), 1705702 (2018). https://doi.org/10.1002/adma.201705702
  50. H. Ge, X. Xie, X. Xie, B. Zhang, S. Li et al., Critical challenges and solutions: quasi-solid-state electrolytes for zinc-based batteries. Energy Environ. Sci. 17(10), 3270–3306 (2024). https://doi.org/10.1039/D4EE00357H
  51. Y. Dai, M. Zhuang, Y.X. Deng, Y. Liao, J. Gu et al., Stable cycling of all-solid-state lithium batteries enabled by cyano-molecular diamond improved polymer electrolytes. Nano-Micro Lett 16(1), 217 (2024). https://doi.org/10.1007/s40820-024-01415-3
  52. Z. Zhang, J. Gou, K. Cui, X. Zhang, Y. Yao et al., 12.6 μm-thick asymmetric composite electrolyte with superior interfacial stability for solid-state lithium-metal batteries. Nano-Micro Lett. 16(1), 181 (2024). https://doi.org/10.1007/s40820-024-01389-2
  53. L. Zhou, A.M. Yao, Y. Wu, Z. Hu, Y. Huang et al., Machine learning assisted prediction of cathode materials for Zn-ion batteries. Adv. Theory Simul. 4(9), 2100196 (2021). https://doi.org/10.1002/adts.202100196
  54. R.P. Joshi, J. Eickholt, L. Li, M. Fornari, V. Barone et al., Machine learning the voltage of electrode materials in metal-ion batteries. ACS Appl. Mater. Interfaces 11(20), 18494–18503 (2019). https://doi.org/10.1021/acsami.9b04933
  55. J. Sturman, C.-H. Yim, E.A. Baranova, Y. Abu-Lebdeh, Communication: design of LiNi0.2Mn0.2Co0.2Fe0.2Ti0.2O2 as a high-entropy cathode for lithium-ion batteries guided by machine learning. J. Electrochem. Soc. 168(5), 050541 (2021). https://doi.org/10.1149/1945-7111/ac00f4
  56. X. Wang, R. Xiao, H. Li, L. Chen, Quantitative structure-property relationship study of cathode volume changes in lithium ion batteries using ab-initio and partial least squares analysis. J. Materiomics 3(3), 178–183 (2017). https://doi.org/10.1016/j.jmat.2017.02.002
  57. T. Sarkar, A. Sharma, A.K. Das, D. Deodhare, M.D. Bharadwaj, A neural network based approach to predict high voltage li-ion battery cathode materials. 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS). March 6–8, 2014, Coimbatore, India. IEEE, (2014). pp 1–3.
  58. O. Allam, B.W. Cho, K.C. Kim, S.S. Jang, Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. RSC Adv. 8(69), 39414–39420 (2018). https://doi.org/10.1039/C8RA07112H
  59. M. Attarian Shandiz, R. Gauvin, Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries. Comput. Mater. Sci. 117, 270–278 (2016). https://doi.org/10.1016/j.commatsci.2016.02.021
  60. R.A. Eremin, P.N. Zolotarev, O.Y. Ivanshina, I.A. Bobrikov, Li(Ni, co, Al)O2 cathode delithiation: a combination of topological analysis, density functional theory, neutron diffraction, and machine learning techniques. J. Phys. Chem. C 121(51), 28293–28305 (2017). https://doi.org/10.1021/acs.jpcc.7b09760
  61. M. Kim, S. Kang, H.G. Park, K. Park, K. Min, Maximizing the energy density and stability of Ni-rich layered cathode materials with multivalent dopants via machine learning. Chem. Eng. J. 452, 139254 (2023). https://doi.org/10.1016/j.cej.2022.139254
  62. N. Artrith, A. Urban, G. Ceder, Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm. J. Chem. Phys. 148(24), 241711 (2018). https://doi.org/10.1063/1.5017661
  63. N. Artrith, A. Urban, Y. Wang, G. Ceder, Atomic-scale factors that control the rate capability of nanostructured amorphous Si for high-energy-density batteries. 1901.09272. (2019)
  64. B. Onat, E.D. Cubuk, B.D. Malone, E. Kaxiras, Implanted neural network potentials: Application to Li-Si alloys. Phys. Rev. B 97(9), 094106 (2018). https://doi.org/10.1103/physrevb.97.094106
  65. J.-X. Huang, G. Csányi, J.-B. Zhao, J. Cheng, V.L. Deringer, First-principles study of alkali-metal intercalation in disordered carbon anode materials. J. Mater. Chem. A 7(32), 19070–19080 (2019). https://doi.org/10.1039/C9TA05453G
  66. R. Wu, S. Seo, L. Ma, J. Bae, T. Kim, Full-fiber auxetic-interlaced yarn sensor for sign-language translation glove assisted by artificial neural network. Nano-Micro Lett. 14(1), 139 (2022). https://doi.org/10.1007/s40820-022-00887-5
  67. Y. Zhang, X. He, Z. Chen, Q. Bai, A.M. Nolan et al., Unsupervised discovery of solid-state lithium ion conductors. Nat. Commun. 10(1), 5260 (2019). https://doi.org/10.1038/s41467-019-13214-1
  68. Z. Lao, K. Tao, X. Xiao, H. Qu, X. Wu et al., Data-driven exploration of weak coordination microenvironment in solid-state electrolyte for safe and energy-dense batteries. Nat. Commun. 16, 1075 (2025). https://doi.org/10.1038/s41467-024-55633-9
  69. A.D. Sendek, Q. Yang, E.D. Cubuk, K.N. Duerloo, Y. Cui et al., Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci. 10(1), 306–320 (2017). https://doi.org/10.1039/C6EE02697D
  70. Z. Zhang, J. Chu, H. Zhang, X. Liu, M. He, Mining ionic conductivity descriptors of antiperovskite electrolytes for all-solid-state batteries via machine learning. J. Energy Storage 75, 109714 (2024). https://doi.org/10.1016/j.est.2023.109714
  71. K. Fujimura, A. Seko, Y. Koyama, A. Kuwabara, I. Kishida et al., Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms. Adv. Energy Mater. 3(8), 980–985 (2013). https://doi.org/10.1002/aenm.201300060
  72. S.R. Xie, S.J. Honrao, J.W. Lawson, High-throughput screening of Li solid-state electrolytes with bond valence methods and machine learning. Chem. Mater. 36(19), 9320–9329 (2024). https://doi.org/10.1021/acs.chemmater.3c02841
  73. X. Guo, Z. Wang, J.-H. Yang, X.-G. Gong, Machine-learning assisted high-throughput discovery of solid-state electrolytes for Li-ion batteries. J. Mater. Chem. A 12(17), 10124–10136 (2024). https://doi.org/10.1039/D4TA00721B
  74. W. Chen, J. Zhou, S. Li, C. Lu, H. Li et al., Accelerated discovery of novel inorganic solid-state electrolytes through machine learning-assisted hierarchical screening. J. Alloys Compd. 1010, 177981 (2025). https://doi.org/10.1016/j.jallcom.2024.177981
  75. K. Hatakeyama-Sato, T. Tezuka, Y. Nishikitani, H. Nishide, K. Oyaizu, Synthesis of lithium-ion conducting polymers designed by machine learning-based prediction and screening. Chem. Lett. 48(2), 130–132 (2019). https://doi.org/10.1246/cl.180847
  76. J.-H. Kim, J. Sun, J. Kim, J.-M. Hong, S. Kang et al., Machine learning-driven discovery of innovative hybrid solid electrolytes for high-performance all-solid-state batteries. Chem. Eng. J. 511, 161926 (2025). https://doi.org/10.1016/j.cej.2025.161926
  77. Y. Wang, T. Xie, A. France-Lanord, A. Berkley, J.A. Johnson et al., Toward designing highly conductive polymer electrolytes by machine learning assisted coarse-grained molecular dynamics. Chem. Mater. 32(10), 4144–4151 (2020). https://doi.org/10.1021/acs.chemmater.9b04830
  78. Y.-T. Chen, M. Duquesnoy, D.H.S. Tan, J.-M. Doux, H. Yang et al., Fabrication of high-quality thin solid-state electrolyte films assisted by machine learning. ACS Energy Lett. 6(4), 1639–1648 (2021). https://doi.org/10.1021/acsenergylett.1c00332
  79. Q. Tu, T. Shi, S. Chakravarthy, G. Ceder, Understanding metal propagation in solid electrolytes due to mixed ionic-electronic conduction. Matter 4(10), 3248–3268 (2021). https://doi.org/10.1016/j.matt.2021.08.004
  80. X. Zhan, S. Lai, M.P. Gobet, S.G. Greenbaum, M. Shirpour, Defect chemistry and electrical properties of garnet-type Li7La3Zr2O12. Phys. Chem. Chem. Phys. 20(3), 1447–1459 (2018). https://doi.org/10.1039/c7cp06768b
  81. X. Zhan, Y.-T. Cheng, M. Shirpour, Nonstoichiometry and Li-ion transport in lithium zirconate: the role of oxygen vacancies. J. Am. Ceram. Soc. 101(9), 4053–4065 (2018). https://doi.org/10.1111/jace.15583
  82. Z. Chen, T. Du, N.M. Anoop Krishnan, Y. Yue, M.M. Smedskjaer, Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes. Nat. Commun. 16(1), 1057 (2025). https://doi.org/10.1038/s41467-025-56322-x
  83. C. Chen, Z. Lu, F. Ciucci, Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12. Sci. Rep. 7, 40769 (2017). https://doi.org/10.1038/srep40769
  84. R. Jalem, M. Kimura, M. Nakayama, T. Kasuga, Informatics-aided density functional theory study on the Li ion transport of tavorite-type LiMTO4F (M3+-T5+, M2+-T6+). J. Chem. Inf. Model. 55(6), 1158–1168 (2015). https://doi.org/10.1021/ci500752n
  85. L. Xiang, Y. Gao, Y. Ding, X. Li, D. Jiang et al., Self-forming Na3P/Na2O interphase on a novel biphasic Na3Zr2Si2PO12/Na3PO4 solid electrolyte for long-cycling solid-state Na-metal batteries. Energy Storage Mater. 73, 103831 (2024). https://doi.org/10.1016/j.ensm.2024.103831
  86. B.-Q. Xiong, S. Chen, X. Luo, Q. Nian, X. Zhan et al., Plastic monolithic mixed-conducting interlayer for dendrite-free solid-state batteries. Adv. Sci. 9(18), 2105924 (2022). https://doi.org/10.1002/advs.202105924
  87. L. Xiang, X. Li, J. Xiao, L. Zhu, X. Zhan, Interface issues and challenges for NASICON-based solid-state sodium-metal batteries. Adv. Powder Mater. 3(3), 100181 (2024). https://doi.org/10.1016/j.apmate.2024.100181
  88. L. Xiang, D. Jiang, Y. Gao, C. Zhang, X. Ren et al., Self-formed fluorinated interphase with Fe valence gradient for dendrite-free solid-state sodium-metal batteries. Adv. Funct. Mater. 34(5), 2301670 (2024). https://doi.org/10.1002/adfm.202301670
  89. R. Li, D. Jiang, P. Du, C. Yuan, X. Cui et al., Negating Na‖Na3Zr2Si2PO12 interfacial resistance for dendrite-free and “Na-less” solid-state batteries. Chem. Sci. 13(47), 14132–14140 (2022). https://doi.org/10.1039/D2SC05120F
  90. K. Kim, N. Adelstein, A. Dive, A. Grieder, S. Kang et al., Probing degradation at solid-state battery interfaces using machine-learning interatomic potential. Energy Storage Mater. 73, 103842 (2024). https://doi.org/10.1016/j.ensm.2024.103842
  91. B. Liu, J. Yang, H. Yang, C. Ye, Y. Mao et al., Rationalizing the interphase stability of Li|doped-Li7La3Zr2O12via automated reaction screening and machine learning. J. Mater. Chem. A 7(34), 19961–19969 (2019). https://doi.org/10.1039/C9TA06748E
  92. S. Wang, K. Xu, H. Song, T. Zhu, Z. Yu et al., A high-energy long-cycling solid-state lithium-metal battery operating at high temperatures. Adv. Energy Mater. 12(38), 2201866 (2022). https://doi.org/10.1002/aenm.202201866
  93. H. Hwang, H. Jeong, J.-W. Cho, Y. Oh, D. Kim et al., Machine learning-assisted microstructural quantification of multiphase cathode composites in all-solid-state batteries: correlation with battery performance. Small 21(10), 2410016 (2025). https://doi.org/10.1002/smll.202410016
  94. F. Zhao, Y. Guo, B. Chen, A review of lithium-ion battery state of charge estimation methods based on machine learning. World Electr. Veh. J. 15(4), 131 (2024). https://doi.org/10.3390/wevj15040131
  95. X. Shu, S. Shen, J. Shen, Y. Zhang, G. Li et al., State of health prediction of lithium-ion batteries based on machine learning: advances and perspectives. iScience 24(11), 103265 (2021). https://doi.org/10.1016/j.isci.2021.103265
  96. Z. Cui, L. Wang, Q. Li, K. Wang, A comprehensive review on the state of charge estimation for lithium-ion battery based on neural network. Int. J. Energy Res. 46(5), 5423–5440 (2022). https://doi.org/10.1002/er.7545
  97. S.-R. Huang, Y.-H. Ma, J.-S. Li, J.-H. Chan, The SOC estimation of LCO battery based on BP neural network. Energy, Transportation and Global Warming. Springer International Publishing, (2016). pp 543–552. https://doi.org/10.1007/978-3-319-30127-3_40
  98. Z. Zhang, S. Chen, L. Lu, X. Han, Y. Li et al., High-precision and robust SOC estimation of LiFePO4 blade batteries based on the BPNN-EKF algorithm. Batteries 9(6), 333 (2023). https://doi.org/10.3390/batteries9060333
  99. M.A. Hannan, M.S.H. Lipu, A. Hussain, M.H. Saad, A. Ayob, Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm. IEEE Access 6, 10069–10079 (2018). https://doi.org/10.1109/ACCESS.2018.2797976
  100. M.S. Hossain Lipu, M.A. Hannan, A. Hussain, M.H.M. Saad, Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection. J. Renew. Sustain. Energy 9(6), 064102 (2017). https://doi.org/10.1063/1.5008491
  101. G. Zhang, B. Xia, J. Wang, Intelligent state of charge estimation of lithium-ion batteries based on L-M optimized back-propagation neural network. J. Energy Storage 44, 103442 (2021). https://doi.org/10.1016/j.est.2021.103442
  102. M.S. Hossain Lipu, M.A. Hannan, A. Hussain, A. Ayob, M.H.M. Saad et al., State of charge estimation in lithium-ion batteries: a neural network optimization approach. Electronics 9(9), 1546 (2020). https://doi.org/10.3390/electronics9091546
  103. D. Cui, B. Xia, R. Zhang, Z. Sun, Z. Lao et al., A novel intelligent method for the state of charge estimation of lithium-ion batteries using a discrete wavelet transform-based wavelet neural network. Energies 11(4), 995 (2018). https://doi.org/10.3390/en11040995
  104. D. Liu, L. Li, Y. Song, L. Wu, Y. Peng, Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions. Int. J. Electr. Power Energy Syst. 110, 48–61 (2019). https://doi.org/10.1016/j.ijepes.2019.02.046
  105. M.A. Hannan, D.N.T. How, M.S. Hossain Lipu, P.J. Ker, Z.Y. Dong et al., SOC estimation of Li-ion batteries with learning rate-optimized deep fully convolutional network. IEEE Trans. Power Electron. 36(7), 7349–7353 (2021). https://doi.org/10.1109/tpel.2020.3041876
  106. S.S. Madani, C. Ziebert, P. Vahdatkhah, S.K. Sadrnezhaad, Recent progress of deep learning methods for health monitoring of lithium-ion batteries. Batteries 10(6), 204 (2024). https://doi.org/10.3390/batteries10060204
  107. C. Shan, C.S. Chin, V. Mohan, C. Zhang, Review of various machine learning approaches for predicting parameters of lithium-ion batteries in electric vehicles. Batteries 10(6), 181 (2024). https://doi.org/10.3390/batteries10060181
  108. W. Liu, Y. Xu, X. Feng, A hierarchical and flexible data-driven method for online state-of-health estimation of Li-ion battery. IEEE Trans. Veh. Technol. 69(12), 14739–14748 (2020). https://doi.org/10.1109/TVT.2020.3037088
  109. X. Li, C. Yuan, Z. Wang, Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression. J. Power. Sources 467, 228358 (2020). https://doi.org/10.1016/j.jpowsour.2020.228358
  110. Y. Zheng, J. Hu, J. Chen, H. Deng, W. Hu, State of health estimation for lithium battery random charging process based on CNN-GRU method. Energy Rep. 9, 1–10 (2023). https://doi.org/10.1016/j.egyr.2022.12.093
  111. J. Zhang, W. Feng, Y. Tan, H. Pan, A health prediction method for new energy vehicle power batteries based on AACNN-LSTM neural network. Int. J. Inf. Commun. Technol. 24(5), 74–94 (2024). https://doi.org/10.1504/ijict.2024.138451
  112. C. Chang, Q. Wang, J. Jiang, T. Wu, Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm. J. Energy Storage 38, 102570 (2021). https://doi.org/10.1016/j.est.2021.102570
  113. V. Yamaçli, State-of-health estimation and classification of series-connected batteries by using deep learning based hybrid decision approach. Heliyon 10(20), e39121 (2024). https://doi.org/10.1016/j.heliyon.2024.e39121
  114. Y. Choi, J. Yun, P. Jang, A deep learning approach for state of health estimation of lithium-ion batteries based on differential thermal voltammetry. IEEE Access 12, 89921–89932 (2024). https://doi.org/10.1109/ACCESS.2024.3419837
  115. S. Wang, R. Zhou, Y. Ren, M. Jiao, H. Liu et al., Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review. Green Chem. Eng. 6(2), 139–153 (2025). https://doi.org/10.1016/j.gce.2024.09.001
  116. D.A. Andrioaia, V.G. Gaitan, G. Culea, I.V. Banu, Predicting the RUL of Li-ion batteries in UAVs using machine learning techniques. Computers 13(3), 64 (2024). https://doi.org/10.3390/computers13030064
  117. L. Ren, L. Zhao, S. Hong, S. Zhao, H. Wang et al., Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access 6, 50587–50598 (2018). https://doi.org/10.1109/ACCESS.2018.2858856
  118. V. Korolev, A. Mitrofanov, A. Korotcov, V. Tkachenko, Graph convolutional neural networks as “general-purpose” property predictors: the universality and limits of applicability. J. Chem. Inf. Model. 60(1), 22–28 (2020). https://doi.org/10.1021/acs.jcim.9b00587
  119. L. Wang, H. Cao, H. Xu, H. Liu, A gated graph convolutional network with multi-sensor signals for remaining useful life prediction. Knowl. Based Syst. 252, 109340 (2022). https://doi.org/10.1016/j.knosys.2022.109340
  120. Y. Wei, D. Wu, Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms. Reliab. Eng. Syst. Saf. 230, 108947 (2023). https://doi.org/10.1016/j.ress.2022.108947
  121. T. Li, Z. Zhao, C. Sun, R. Yan, X. Chen, Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction. Reliab. Eng. Syst. Saf. 215, 107878 (2021). https://doi.org/10.1016/j.ress.2021.107878
  122. Y. Wei, D. Wu, State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network. Expert Syst. Appl. 238, 122041 (2024). https://doi.org/10.1016/j.eswa.2023.122041
  123. K. Park, Y. Choi, W.J. Choi, H.-Y. Ryu, H. Kim, LSTM-based battery remaining useful life prediction with multi-channel charging profiles. IEEE Access 8, 20786–20798 (2020). https://doi.org/10.1109/ACCESS.2020.2968939
  124. F.-K. Wang, Z.E. Amogne, J.-H. Chou, C. Tseng, Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism. Energy 254, 124344 (2022). https://doi.org/10.1016/j.energy.2022.124344
  125. L. Ma, J. Tian, T. Zhang, Q. Guo, C. Hu, Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning. J. Energy Chem. 91, 512–521 (2024). https://doi.org/10.1016/j.jechem.2023.12.043
  126. D. Cheng, W. Sha, L. Wang, S. Tang, A. Ma et al., Solid-state lithium battery cycle life prediction using machine learning. Appl. Sci. 11(10), 4671 (2021). https://doi.org/10.3390/app11104671
  127. Q. Qi, W. Liu, Z. Deng, Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data. J. Energy Chem. 92, 605–618 (2024). https://doi.org/10.1016/j.jechem.2024.01.047
  128. D. Ge, Z. Zhang, X. Kong, Z. Wan, Extreme learning machine using bat optimization algorithm for estimating state of health of lithium-ion batteries. Appl. Sci. 12(3), 1398 (2022). https://doi.org/10.3390/app12031398
  129. Y. Ma, L. Wu, Y. Guan, Z. Peng, The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach. J. Power. Sour. 476, 228581 (2020). https://doi.org/10.1016/j.jpowsour.2020.228581
  130. S. Shen, M. Sadoughi, M. Li, Z. Wang, C. Hu, Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries. Appl. Energy 260, 114296 (2020). https://doi.org/10.1016/j.apenergy.2019.114296
  131. V. Vakharia, M. Shah, P. Nair, H. Borade, P. Sahlot et al., Estimation of lithium-ion battery discharge capacity by integrating optimized explainable-AI and stacked LSTM model. Batteries 9(2), 125 (2023). https://doi.org/10.3390/batteries9020125
  132. S. Oyucu, B. Ersöz, Ş Sağıroğlu, A. Aksöz, E. Biçer, Optimizing lithium-ion battery performance: integrating machine learning and explainable AI for enhanced energy management. Sustainability 16(11), 4755 (2024). https://doi.org/10.3390/su16114755
  133. G. Crocioni, D. Pau, J.-M. Delorme, G. Gruosso, Li-ion batteries parameter estimation with tiny neural networks embedded on intelligent IoT microcontrollers. IEEE Access 8, 122135–122146 (2020). https://doi.org/10.1109/ACCESS.2020.3007046
  134. M. Pal, G.M. Foody, Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 5(5), 1344–1355 (2012). https://doi.org/10.1109/JSTARS.2012.2215310
  135. Y. Chang, H. Fang, Y. Zhang, A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery. Appl. Energy 206, 1564–1578 (2017). https://doi.org/10.1016/j.apenergy.2017.09.106
  136. P. Guo, Z. Cheng, L. Yang, A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction. J. Power. Sour. 412, 442–450 (2019). https://doi.org/10.1016/j.jpowsour.2018.11.072
  137. R. Chen, Q. Li, X. Yu, L. Chen, H. Li, Approaching practically accessible solid-state batteries: stability issues related to solid electrolytes and interfaces. Chem. Rev. 120(14), 6820–6877 (2020). https://doi.org/10.1021/acs.chemrev.9b00268
  138. A. Benrath, K. Drekopf, On the electric conductivity of salts and salt mixtures. Z. Phys. Chem. 99, 57–70 (1921). https://doi.org/10.1515/zpch-1921-9904
  139. Y. Haven, The ionic conductivity of Li-halide crystals. Recl. Des Trav. Chim. Des Pays Bas 69(12), 1471–1489 (1950). https://doi.org/10.1002/recl.19500691203
  140. C.C. Liang, Conduction characteristics of the lithium iodide-aluminum oxide solid electrolytes. J. Electrochem. Soc. 120(10), 1289 (1973). https://doi.org/10.1149/1.2403248
  141. J.B. Goodenough, H.Y. Hong, J.A. Kafalas, Fast Na+-ion transport in skeleton structures. Mater. Res. Bull. 11(2), 203–220 (1976). https://doi.org/10.1016/0025-5408(76)90077-5
  142. M.S. Whittingham, Electrical energy storage and intercalation chemistry. Science 192(4244), 1126–1127 (1976). https://doi.org/10.1126/science.192.4244.1126
  143. H.Y. Hong, Crystal structure and ionic conductivity of Li14Zn(GeO4)4 and other new Li+ superionic conductors. Mater. Res. Bull. 13(2), 117–124 (1978). https://doi.org/10.1016/0025-5408(78)90075-2
  144. M.B. Armand, M.J. Duclot, P. Rigaud, Polymer solid electrolytes: stability domain. Solid State Ion. 3, 429–430 (1981). https://doi.org/10.1016/0167-2738(81)90126-0
  145. K. Mizushima, P.C. Jones, P.J. Wiseman, J.B. Goodenough, LixCoO2 (0new cathode material for batteries of high energy density. Mater. Res. Bull. 15(6), 783–789 (1980)
  146. R. Mercier, J.-P. Malugani, B. Fahys, G. Robert, J. Douglade, Structure du tetrathiophosphate de lithium. Acta Crystallogr. Sect. B Struct. Crystallogr. Cryst. Chem. 38(7), 1887–1890 (1982). https://doi.org/10.1107/s0567740882007535
  147. L. Latie, G. Villeneuve, D. Conte, G. Le Flem, Ionic conductivity of oxides with general formula Li xLn1/3Nb1−x TixO3 (Ln = La, Nd). J. Solid State Chem. 51(3), 293–299 (1984). https://doi.org/10.1016/0022-4596(84)90345-1
  148. H. Aono, E. Sugimoto, Y. Sadaoka, N. Imanaka, G.-Y. Adachi, Ionic conductivity of solid electrolytes based on lithium titanium phosphate. J. Electrochem. Soc. 137(4), 1023–1027 (1990). https://doi.org/10.1149/1.2086597
  149. J.B. Bates, N.J. Dudney, G.R. Gruzalski, R.A. Zuhr, A. Choudhury et al., Electrical properties of amorphous lithium electrolyte thin films. Solid State Ion. 53, 647–654 (1992). https://doi.org/10.1016/0167-2738(92)90442-R
  150. Y. Inaguma, L. Chen, M. Itoh, T. Nakamura, T. Uchida et al., High ionic conductivity in lithium lanthanum titanate. Solid State Commun. 86(10), 689–693 (1993). https://doi.org/10.1016/0038-1098(93)90841-A
  151. R. Kanno, T. Hata, Y. Kawamoto, M. Irie, Synthesis of a new lithium ionic conductor, thio-LISICON–lithium germanium sulfide system. Solid State Ion. 130(1–2), 97–104 (2000). https://doi.org/10.1016/S0167-2738(00)00277-0
  152. N. Kamaya, K. Homma, Y. Yamakawa, M. Hirayama, R. Kanno et al., A lithium superionic conductor. Nat. Mater. 10(9), 682–686 (2011). https://doi.org/10.1038/nmat3066
  153. Y. Zhao, L.L. Daemen, Superionic conductivity in lithium-rich anti-perovskites. J. Am. Chem. Soc. 134(36), 15042–15047 (2012). https://doi.org/10.1021/ja305709z
  154. Y. Kato, S. Hori, T. Saito, K. Suzuki, M. Hirayama et al., High-power all-solid-state batteries using sulfide superionic conductors. Nat. Energy 1, 16030 (2016). https://doi.org/10.1038/nenergy.2016.30
  155. C.C. Liang, J. Epstein, G.H. Boyle, A high-voltage, solid-state battery system. J. Electrochem. Soc. 116(10), 1452 (1969). https://doi.org/10.1149/1.2411560
  156. K. Kanehori, K. Matsumoto, K. Miyauchi, T. Kudo, Thin film solid electrolyte and its application to secondary lithium cell. Solid State Ion. 9, 1445–1448 (1983). https://doi.org/10.1016/0167-2738(83)90192-3
  157. P. Birke, F. Salam, S. Döring, W. Weppner, A first approach to a monolithic all solid state inorganic lithium battery. Solid State Ion. 118(1–2), 149–157 (1999). https://doi.org/10.1016/S0167-2738(98)00462-7
  158. K. Yoshima, Y. Harada, N. Takami, Thin hybrid electrolyte based on garnet-type lithium-ion conductor Li7La3Zr2O12 for 12 V-class bipolar batteries. J. Power. Sour. 302, 283–290 (2016). https://doi.org/10.1016/j.jpowsour.2015.10.031
  159. B. Wu, S. Wang, W.J. Evans IV., D.Z. Deng, J. Yang et al., Interfacial behaviours between lithium ion conductors and electrode materials in various battery systems. J. Mater. Chem. A 4(40), 15266–15280 (2016). https://doi.org/10.1039/C6TA05439K
  160. M. Pasta, D. Armstrong, Z.L. Brown, J. Bu, M.R. Castell et al., 2020 roadmap on solid-state batteries. J. Phys. Energy 2(3), 032008 (2020). https://doi.org/10.1088/2515-7655/ab95f4
  161. Y. Lu, C.-Z. Zhao, H. Yuan, J.-K. Hu, J.-Q. Huang et al., Dry electrode technology, the rising star in solid-state battery industrialization. Matter 5(3), 876–898 (2022). https://doi.org/10.1016/j.matt.2022.01.011
  162. D. Xiao, B. Li, J. Shan, Z. Yan, J. Huang, SOC estimation of vanadium redox flow batteries based on the ISCSO-ELM algorithm. ACS Omega 8(48), 45708–45714 (2023). https://doi.org/10.1021/acsomega.3c06113
  163. S. Kalnaus, N.J. Dudney, A.S. Westover, E. Herbert, S. Hackney, Solid-state batteries: the critical role of mechanics. Science 381(6664), eabg5998 (2023). https://doi.org/10.1126/science.abg5998
  164. G. Nazir, A. Rehman, J.H. Lee, C.H. Kim, J. Gautam et al., A review of rechargeable zinc-air batteries: recent progress and future perspectives. Nano-Micro Lett 16(1), 138 (2024). https://doi.org/10.1007/s40820-024-01328-1
  165. O. Dorabiala, A.Y. Aravkin, J.N. Kutz, Ensemble principal component analysis. IEEE Access 12, 6663–6671 (2024). https://doi.org/10.1109/access.2024.3350984
  166. J. Zheng, Z. Yang, Z. Ge, Deep residual principal component analysis as feature engineering for industrial data analytics. IEEE Trans. Instrum. Meas. 73, 2523310 (2024). https://doi.org/10.1109/TIM.2024.3420267
  167. S. Zhao, B. Zhang, J. Yang, J. Zhou, Y. Xu, Linear discriminant analysis. Nat. Rev. Meth. Primers 4, 70 (2024). https://doi.org/10.1038/s43586-024-00346-y
  168. R. Graf, M. Zeldovich, S. Friedrich, Comparing linear discriminant analysis and supervised learning algorithms for binary classification-a method comparison study. Biom. J. 66(1), e2200098 (2024). https://doi.org/10.1002/bimj.202200098
  169. D. Theng, K.K. Bhoyar, Feature selection techniques for machine learning: a survey of more than two decades of research. Knowl. Inf. Syst. 66(3), 1575–1637 (2024). https://doi.org/10.1007/s10115-023-02010-5
  170. Y. Fan, J. Liu, J. Tang, P. Liu, Y. Lin et al., Learning correlation information for multi-label feature selection. Pattern Recognit. 145, 109899 (2024). https://doi.org/10.1016/j.patcog.2023.109899
  171. A. Wiles, F. Colombo, R. Mascorro, Ransomware detection using network traffic analysis and generative adversarial networks. September 17, 2024. https://doi.org/10.22541/au.172659907.77469627/v1
  172. A. Aggarwal, M. Mittal, G. Battineni, Generative adversarial network: an overview of theory and applications. Int. J. Inf. Manag. Data Insights 1(1), 100004 (2021). https://doi.org/10.1016/j.jjimei.2020.100004
  173. E.R. Chan, M. Monteiro, P. Kellnhofer, J. Wu, G. Wetzstein, Pi-GAN: periodic implicit generative adversarial networks for 3D-aware image synthesis. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 20–25, 2021. Nashville, TN, USA. IEEE, (2021). 5795–5805. https://doi.org/10.1109/cvpr46437.2021.00574
  174. V. Hassija, V. Chamola, A. Mahapatra, A. Singal, D. Goel et al., Interpreting black-box models: a review on explainable artificial intelligence. Cogn. Comput. 16(1), 45–74 (2024). https://doi.org/10.1007/s12559-023-10179-8
  175. C. Zednik, Solving the black box problem: a normative framework for explainable artificial intelligence. Philos. Technol. 34(2), 265–288 (2021). https://doi.org/10.1007/s13347-019-00382-7
  176. S. Vollert, M. Atzmueller, A. Theissler, Interpretable Machine Learning: a brief survey from the predictive maintenance perspective. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). September 7–10, 2021, Vasteras, Sweden. IEEE, (2021). pp 01–08. https://doi.org/10.1109/ETFA45728.2021.9613467
  177. N. Aslam, I.U. Khan, S. Mirza, A. AlOwayed, F.M. Anis et al., Interpretable machine learning models for malicious domains detection using explainable artificial intelligence (XAI). Sustainability 14(12), 7375 (2022). https://doi.org/10.3390/su14127375
  178. Y. Ma, Y. Qiu, K. Yang, S. Lv, Y. Li et al., Competitive Li-ion coordination for constructing a three-dimensional transport network to achieve ultra-high ionic conductivity of a composite solid-state electrolyte. Energy Environ. Sci. 17(21), 8274–8283 (2024). https://doi.org/10.1039/D4EE03134B
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