AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Nano Research Energy Article
PDF (3.1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Review Article | Open Access

Designing electrodes and electrolytes for batteries by leveraging deep learning

Chenxi Sui1,§, Ziyang Jiang2,§, Genesis Higueros1,3,§, David Carlson2( ), Po-Chun Hsu1( )
Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA

§ Chenxi Sui, Ziyang Jiang, and Genesis Higueros contributed equally to this work.

Show Author Information

Graphical Abstract

Recent advancements in deep learning techniques offer promising solutions for the challenging task of optimizing batteries, particularly in improving electrodes and electrolytes. This review comprehensively explores the application of deep learning principles in addressing electrochemical problems related to batteries, bridging the gap between artificial intelligence and electrochemistry, and aims to inspire future progress in both scientific understanding and practical engineering in the field of battery technology.

Abstract

High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries, particularly in optimizing electrodes and electrolytes. This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems, which bridges the gap between artificial intelligence and electrochemistry. We also examine the potential challenges and opportunities associated with different deep learning approaches, tailoring them to specific battery requirements. Ultimately, we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology. Furthermore, we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering.

Keywords

battery design, fast-charging battery, three-dimensional (3D) structure, deep learning, optimization

References

[1]

van Soest, H. L. ; den Elzen, M. G. J.; van Vuuren, D. P. Net-zero emission targets for major emitting countries consistent with the Paris Agreement. Nat. Commun. 2021, 12, 2140.
Crossref Google Scholar
[2]
United Nations Sustainable Development Goals [Online]. https://sdgs.un.org/goals (accessed June 1, 2023).
[3]
Renewable Electricity-Analysis. International Energy Agency [Online]. https://www.iea.org/reports/renewables-2022/renewable-electricity (accessed May 10, 2023).
[4]
IEA. Global EV Outlook 2021; International Energy Agency: Paris, 2021.
[5]

Nykvist, B.; Nilsson, M. Rapidly falling costs of battery packs for electric vehicles. Nat. Climate Change 2015, 5, 329–332.
Crossref Google Scholar
[6]

Deng, W.; Yin, X.; Bao, W.; Zhou, X. F.; Hu, Z. Y.; He, B. Y.; Qiu, B.; Meng, Y. S.; Liu, Z. P. Quantification of reversible and irreversible lithium in practical lithium-metal batteries. Nat. Energy 2022, 7, 1031–1041.
Crossref Google Scholar
[7]

Kim, M. S.; Zhang, Z. W.; Wang, J. Y.; Oyakhire, S. T.; Kim, S. C.; Yu, Z. A.; Chen, Y. L.; Boyle, D. T.; Ye, Y. S.; Huang, Z. J. et al. Revealing the multifunctions of Li3N in the suspension electrolyte for lithium metal batteries. ACS Nano 2023, 17, 3168–3180.
Crossref Google Scholar
[8]

Shimizu, R.; Cheng, D. Y.; Weaver, J. L.; Zhang, M. H.; Lu, B. Y.; Wynn, T. A.; Burger, R.; Kim, M. C.; Zhu, G. M.; Meng, Y. S. Unraveling the stable cathode electrolyte interface in all solid-state thin-film battery operating at 5 V. Adv. Energy Mater. 2022, 12, 2201119.
Crossref Google Scholar
[9]

Wang, C. Y.; Liu, T.; Yang, X. G.; Ge, S. H.; Stanley, N. V.; Rountree, E. S.; Leng, Y. J.; Mccarthy, B. D. Fast charging of energy-dense lithium-ion batteries. Nature 2022, 611, 485–490.
Crossref Google Scholar
[10]

Wang, H. S.; Yu, Z. A.; Kong, X.; Kim, S. C.; Boyle, D. T.; Qin, J.; Bao, Z. N.; Cui, Y. Liquid electrolyte: The nexus of practical lithium metal batteries. Joule 2022, 6, 588–616.
Crossref Google Scholar
[11]

Xiao, J.; Shi, F. F.; Glossmann, T.; Burnett, C.; Liu, Z. From laboratory innovations to materials manufacturing for lithium-based batteries. Nat. Energy 2023, 8, 329–339.
Crossref Google Scholar
[12]

Zhou, G. M.; Chen, H.; Cui, Y. Formulating energy density for designing practical lithium-sulfur batteries. Nat. Energy 2022, 7, 312–319.
Crossref Google Scholar
[13]

Chen, C. B.; Yu, S.; Yang, Y.; Louisia, S.; Roh, I.; Jin, J. B.; Chen, S. P.; Chen, P. C.; Shan, Y.; Yang, P. D. Exploration of the bio-analogous asymmetric C–C coupling mechanism in tandem CO2 electroreduction. Nat. Catal. 2022, 5, 878–887.
Crossref Google Scholar
[14]

Choi, C.; Kwon, S.; Cheng, T.; Xu, M. J.; Tieu, P.; Lee, C.; Cai, J.; Lee, H. M.; Pan, X. Q.; Duan, X. F. et al. Highly active and stable stepped Cu surface for enhanced electrochemical CO2 reduction to C2H4. Nat. Catal. 2020, 3, 804–812.
Crossref Google Scholar
[15]

Le, D.; Rahman, T. S. On the role of metal cations in CO2 electrocatalytic reduction. Nat. Catal. 2022, 5, 977–978.
Crossref Google Scholar
[16]

Xie, Y.; Ou, P. F.; Wang, X.; Xu, Z. Y.; Li, Y. C.; Wang, Z. Y.; Huang, J. E.; Wicks, J.; Mccallum, C.; Wang, N. et al. High carbon utilization in CO2 reduction to multi-carbon products in acidic media. Nat. Catal. 2022, 5, 564–570.
Crossref Google Scholar
[17]

Han, G. Q.; Li, G. D.; Sun, Y. J. Electrocatalytic dual hydrogenation of organic substrates with a Faradaic efficiency approaching 200%. Nat. Catal. 2023, 6, 224–233.
Crossref Google Scholar
[18]

Lazaridis, T.; Stühmeier, B. M.; Gasteiger, H. A.; El-Sayed, H. A. Capabilities and limitations of rotating disk electrodes versus membrane electrode assemblies in the investigation of electrocatalysts. Nat. Catal. 2022, 5, 363–373.
Crossref Google Scholar
[19]

McCrum, I. T.; Koper, M. T. M. The role of adsorbed hydroxide in hydrogen evolution reaction kinetics on modified platinum. Nat. Energy 2020, 5, 891–899.
Crossref Google Scholar
[20]

Tang, B. Y.; Bisbey, R. P.; Lodaya, K. M.; Toh, W. L.; Surendranath, Y. Reaction environment impacts charge transfer but not chemical reaction steps in hydrogen evolution catalysis. Nat. Catal. 2023, 6, 339–350.
Crossref Google Scholar
[21]

Sui, C. X.; Hsu, P.-C. Radiative electrochromism for energy-efficient buildings. Nat. Sustain. 2023, 6, 358–359.
Crossref Google Scholar
[22]

Barile, C. J.; Slotcavage, D. J.; Hou, J. Y.; Strand, M. T.; Hernandez, T. S.; Mcgehee, M. D. Dynamic windows with neutral color, high contrast, and excellent durability using reversible metal electrodeposition. Joule 2017, 1, 133–145.
Crossref Google Scholar
[23]

Barile, C. J.; Slotcavage, D. J.; McGehee, M. D. Polymer-nanoparticle electrochromic materials that selectively modulate visible and near-infrared light. Chem. Mater. 2016, 28, 1439–1445.
Crossref Google Scholar
[24]

Hernandez, T. S.; Barile, C. J.; Strand, M. T.; Dayrit, T. E.; Slotcavage, D. J.; Mcgehee, M. D. Bistable black electrochromic windows based on the reversible metal electrodeposition of Bi and Cu. ACS Energy Lett. 2018, 3, 104–111.
Crossref Google Scholar
[25]

Madu, D. C.; Lilo, M. V.; Thompson, A. A.; Pan, H. Q.; Mcgehee, M. D.; Barile, C. J. Investigating formate, sulfate, and halide anions in reversible zinc electrodeposition dynamic windows. ACS Appl. Mater. Interfaces 2022, 14, 47810–47821.
Crossref Google Scholar
[26]

Sui, C. X.; Pu, J. K.; Chen, T. H.; Liang, J. W.; Lai, Y. T.; Rao, Y. F.; Wu, R. H.; Han, Y.; Wang, K. Y.; Li, X. Q. et al. Dynamic electrochromism for all-season radiative thermoregulation. Nat. Sustain. 2023, 6, 428–437.
Crossref Google Scholar
[27]

Nature Sustainability. Research for greener batteries. Nat. Sustain. 2021, 4, 373.
Crossref Google Scholar
[28]

Liu, Y. Y.; Zhu, Y. Y.; Cui, Y. Challenges and opportunities towards fast-charging battery materials. Nat. Energy 2019, 4, 540–550.
Crossref Google Scholar
[29]

Trahey, L.; Brushett, F. R.; Balsara, N. P.; Ceder, G.; Cheng, L.; Chiang, Y. M.; Hahn, N. T.; Ingram, B. J.; Minteer, S. D.; Moore, J. S. et al. Energy storage emerging: A perspective from the Joint Center for Energy Storage Research. Proc. Natl. Acad. Sci. USA 2020, 117, 12550–12557.
Crossref Google Scholar
[30]

Ramadesigan, V.; Northrop, P. W. C.; De, S.; Santhanagopalan, S.; Braatz, R. D.; Subramanian, V. R. Modeling and simulation of lithium-ion batteries from a systems engineering perspective. J. Electrochem. Soc. 2012, 159, R31–R45.
Crossref Google Scholar
[31]

Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater. 2016, 4, 053208.
Crossref Google Scholar
[32]

Aykol, M.; Hegde, V. I.; Hung, L.; Suram, S.; Herring, P.; Wolverton, C.; Hummelshøj, J. S. Network analysis of synthesizable materials discovery. Nat. Commun. 2019, 10, 2018.
Crossref Google Scholar
[33]

Balachandran, P. V.; Kowalski, B.; Sehirlioglu, A.; Lookman, T. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat. Commun. 2018, 9, 1668.
Crossref Google Scholar
[34]

Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018, 559, 547–555.
Crossref Google Scholar
[35]

Carrete, J.; Li, W.; Mingo, N.; Wang, S. D.; Curtarolo, S. Finding unprecedentedly low-thermal-conductivity half-heusler semiconductors via high-throughput materials modeling. Phys. Rev. X 2014, 4, 011019.
Crossref Google Scholar
[36]

Coley, C. W.; Jin, W. G.; Rogers, L.; Jamison, T. F.; Jaakkola, T. S.; Green, W. H.; Barzilay, R.; Jensen, K. F. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 2019, 10, 370–377.
Crossref Google Scholar
[37]

Granda, J. M.; Donina, L.; Dragone, V.; Long, D. L.; Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 2018, 559, 377–381.
Crossref Google Scholar
[38]

Isayev, O.; Oses, C.; Toher, C.; Gossett, E.; Curtarolo, S.; Tropsha, A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 2017, 8, 15679.
Crossref Google Scholar
[39]

Kim, C.; Pilania, G.; Ramprasad, R. Machine learning assisted predictions of intrinsic dielectric breakdown strength of ABX3 perovskites. J. Phys. Chem. C 2016, 120, 14575–14580.
Crossref Google Scholar
[40]

Raccuglia, P.; Elbert, K. C.; Adler, P. D. F.; Falk, C.; Wenny, M. B.; Mollo, A.; Zeller, M.; Friedler, S. A.; Schrier, J.; Norquist, A. J. Machine-learning-assisted materials discovery using failed experiments. Nature 2016, 533, 73–76.
Crossref Google Scholar
[41]

Schmidt, J.; Marques, M. R. G.; Botti, S.; Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 2019, 5, 83.
Crossref Google Scholar
[42]

Wang, A. Y. T.; Murdock, R. J.; Kauwe, S. K.; Oliynyk, A. O.; Gurlo, A.; Brgoch, J.; Persson, K. A.; Sparks, T. D. Machine learning for materials scientists: An introductory guide toward best practices. Chem. Mater. 2020, 32, 4954–4965.
Crossref Google Scholar
[43]

Ward, L.; Aykol, M.; Blaiszik, B.; Foster, I.; Meredig, B.; Saal, J.; Suram, S. Strategies for accelerating the adoption of materials informatics. MRS Bull. 2018, 43, 683–689.
Crossref Google Scholar
[44]

Xue, D. Z.; Balachandran, P. V.; Hogden, J.; Theiler, J.; Xue, D. Q.; Lookman, T. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 2016, 7, 11241.
Crossref Google Scholar
[45]

Schütt, K. T.; Arbabzadah, F.; Chmiela, S.; Müller, K. R.; Tkatchenko, A. Quantum-chemical insights from deep tensor neural networks. Nat. Commun. 2017, 8, 13890.
Crossref Google Scholar
[46]

Attia, P. M.; Grover, A.; Jin, N.; Severson, K. A.; Markov, T. M.; Liao, Y. H.; Chen, M. H.; Cheong, B.; Perkins, N.; Yang, Z. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 2020, 578, 397–402.
Crossref Google Scholar
[47]

Severson, K. A.; Attia, P. M.; Jin, N.; Perkins, N.; Jiang, B. B.; Yang, Z.; Chen, M. H.; Aykol, M.; Herring, P. K.; Fraggedakis, D. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 2019, 4, 383–391.
Crossref Google Scholar
[48]

Bhowmik, A.; Castelli, I. E.; Garcia-Lastra, J. M.; Jørgensen, P. B.; Winther, O.; Vegge, T. A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning. Energy Storage Mater. 2019, 21, 446–456.
Crossref Google Scholar
[49]

Kim, S. C.; Oyakhire, S. T.; Athanitis, C.; Wang, J. Y.; Zhang, Z. W.; Zhang, W. B.; Boyle, D. T.; Kim, M. S.; Yu, Z. A.; Gao, X. et al. Data-driven electrolyte design for lithium metal anodes. Proc. Natl. Acad. Sci. USA 2023, 120, e2214357120.
Crossref Google Scholar
[50]

Zhang, D. W.; Zhong, C.; Xu, P. J.; Tian, Y. Y. Deep learning in the state of charge estimation for Li-ion batteries of electric vehicles: A review. Machines 2022, 10, 912.
Crossref Google Scholar
[51]

Scharf, J.; Chouchane, M.; Finegan, D. P.; Lu, B. Y.; Redquest, C.; Kim, M. C.; Yao, W. L.; Franco, A. A.; Gostovic, D.; Liu, Z. et al. Bridging nano-and microscale X-ray tomography for battery research by leveraging artificial intelligence. Nat. Nanotechnol. 2022, 17, 446–459.
Crossref Google Scholar
[52]
Bishop, C. M. Pattern Recognition and Machine Learning; Springer: New York, 2006.
[53]
Rudin, C.; Carlson, D. The secrets of machine learning: Ten things you wish you had known earlier to be more effective at data analysis. In Proceedings of the Operations Research & Management Science in the Age of Analytics, Catonsville, USA, 2019, pp 44–72.
Crossref
[54]
Drucker, H.; Burges, C. J. C.; Kaufman, L.; Smola, A.; Vapnik, V. Support vector regression machines. In Proceedings of the 9 th International Conference on Neural Information Processing Systems, Denver, USA, 1996, pp 155–161.
[55]

Hoerl, A. E.; Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970, 12, 55–67.
Crossref Google Scholar
[56]

Tibshirani, R. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc.: Ser. B Methodol. 1996, 58, 267–288.
Crossref Google Scholar
[57]
Rasmussen, C. E.; Williams, C. K. I. Model Selection and Adaptation of Hyperparameters; MIT Press: Cambridge, 2005.
[58]
Jiang, Z. Y.; Zheng, T. S.; Liu, Y. L.; Carlson, D. Incorporating prior knowledge into neural networks through an implicit composite kernel. 2022, arXiv preprint aarXiv:2205.07384. arXiv.org e-Print archive. https://doi.org/10.48550/arXiv.2205.07384 (accessed May 12, 2023).
[59]
Frazier, P. I. A tutorial on bayesian optimization. 2018, arXiv:1807.02811. arXiv.org e-Print archive. https://doi.org/10.48550/arXiv.1807.02811 (accessed May 11, 2023).
[60]
Snoek, J.; Larochelle, H.; Adams, R. P. Practical bayesian optimization of machine learning algorithms. In Proceedings of the 25 th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2012, pp 2951–2959.
[61]

LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
Crossref Google Scholar
[62]
Lin, Y.; Dong, H. Z.; Wang, H.; Zhang, T. Bayesian invariant risk minimization. In Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022, pp 16000–16009.
Crossref
[63]
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; Sutskever, I. Zero-shot text-to-image generation. In Proceedings of the 38 th International Conference on Machine Learning, Virtual, 18–24 July 2021, pp 8821–8831.
[64]
Sohl-Dickstein, J.; Weiss, E. A.; Maheswaranathan, N.; Ganguli, S. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015, pp 2256–2265.
[65]
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A. et al. Language models are few-shot learners. In Proceedings of the 34 th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, pp 159.
[66]
Devlin, J.; Chang, M. W.; Lee, K.; Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, USA, 2018, pp 4171–4186.
[67]
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. In Proceedings of the 31 st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017, pp 6000–6010.
[68]
Hatt, T.; Feuerriegel, S. Estimating average treatment effects via orthogonal regularization. In Proceedings of the 30 th ACM International Conference on Information & Knowledge Management, Queensland, Australia, 2021, pp 680–689.
Crossref
[69]
Jiang, Z. Y.; Hou, Z. R.; Liu, Y. L.; Ren, Y. M.; Li, K. Y.; Carlson, D. E. Estimating causal effects using a multi-task deep ensemble. In Proceedings of the International Conference on Machine Learning, Honolulu, USA, 2023, pp 15023–15040.
[70]
Louizos, C.; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M. Causal effect inference with deep latent-variable models. In Proceedings of the 31 st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017, pp 6449–6459.
[71]
Shalit, U.; Johansson, F. D.; Sontag, D. Estimating individual treatment effect: Generalization bounds and algorithms. In Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia, 2017, pp 3076–3085.
[72]
Jiang, Z. Y.; Liu, Y. L.; Klein, M. H.; Aloui, A.; Ren, Y. M.; Li, K. Y.; Tarokh, V.; Carlson, D. Causal mediation analysis with multi-dimensional and indirectly observed mediators. 2023, arXiv:2306.07918. arXiv.org e-Print archive. https://arxiv.org/abs/2306.07918 (accessed April 20, 2023).
[73]
Ahmad, M. A.; Eckert, C.; Teredesai, A. Interpretable machine learning in healthcare. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Washington, DC, USA, 2018, pp 559–560.
Crossref
[74]

Erickson, B. J.; Korfiatis, P.; Akkus, Z.; Kline, T. L. Machine learning for medical imaging. Radiographics 2017, 37, 505–515.
Crossref Google Scholar
[75]

Miotto, R.; Wang, F.; Wang, S.; Jiang, X. Q.; Dudley, J. T. Deep learning for healthcare: Review, opportunities and challenges. Brief. Bioinform. 2018, 19, 1236–1246.
Crossref Google Scholar
[76]

Jiang, Z. Y.; Zheng, T. S.; Bergin, M.; Carlson, D. Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery. Sci. Remote Sens. 2022, 5, 100052.
Crossref Google Scholar
[77]

Zheng, T. S.; Bergin, M.; Wang, G. Y.; Carlson, D. Local PM2.5 hotspot detector at 300 m resolution: A random forest-convolutional neural network joint model jointly trained on satellite images and meteorology. Remote Sens. 2021, 13, 1356.
Crossref Google Scholar
[78]

Zheng, T. S.; Bergin, M. H.; Hu, S. J.; Miller, J.; Carlson, D. E. Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach. Atmos. Environ. 2020, 230, 117451.
Crossref Google Scholar
[79]
Agarap, A. F. Deep learning using rectified linear units (ReLU). 2018, arXiv:1803.08375. arXiv.org e-Print archive. https://doi.org/10.48550/arXiv.1803.08375 (accessed April 1, 2023).
[80]
Maas, A. L.; Hannun, A. Y.; Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the 30 th International Conference on Machine Learning, Atlanta, USA, 2013, pp 3.
[81]

Karlik, B.; Vehbi, A. Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 2011, 1, 111–122.
Google Scholar
[82]

Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning representations by back-propagating errors. Nature 1986, 323, 533–536.
Crossref Google Scholar
[83]

Le Cun, Y.; Jackel, L. D.; Boser, B.; Denker, J. S.; Graf, H. P.; Guyon, I.; Henderson, D.; Howard, R. E.; Hubbard, W. Handwritten digit recognition: Applications of neural network chips and automatic learning. IEEE Commun. Mag. 1989, 27, 41–46.
Crossref Google Scholar
[84]
LeCun, Y.; Bengio, Y. Convolutional networks for images, speech, and time-series. In Proceedings of the Handbook of Brain Theory and Neural Networks, Cambridge, USA, 1998, pp 255–258.
[85]

Jordan, M. I. Serial order: A parallel distributed processing approach. Adv. Psychol. 1997, 121, 471–495.
Crossref Google Scholar
[86]

Werbos, P. J. Backpropagation through time: What it does and how to do it. Proc. IEEE 1990, 78, 1550–1560.
Crossref Google Scholar
[87]

Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780.
Crossref Google Scholar
[88]
Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014, arXiv:1412.3555. arXiv.org e-Print archive. https://doi.org/10.48550/arXiv.1412.3555 (accessed March 19, 2023).
[89]
Jozefowicz, R.; Zaremba, W.; Sutskever, I. An empirical exploration of recurrent network architectures. In Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015, pp 2342–2350.
[90]
Bahdanau, D.; Cho, K.; Bengio, Y. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3 rd International Conference on Learning Representations, San Diego, USA, 2015.
[91]
Masci, J.; Meier, U.; Cireşan, D.; Schmidhuber, J. Stacked convolutional auto-encoders for hierarchical feature extraction. In Proceedings of the 21 st International Conference on Artificial Neural Networks, Espoo, Finland, 2011, pp 52–59.
Crossref
[92]
Kingma, D. P.; Welling, M. Auto-encoding variational bayes. In Proceedings of the 2 nd International Conference on Learning Representations, Banff, Canada, 2014.
[93]

Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative adversarial networks. Commun. ACM 2020, 63, 139–144.
Crossref Google Scholar
[94]
Arora, S.; Risteski, A.; Zhang, Y. Do GANs learn the distribution? Some theory and empirics. In Proceedings of the 6 th International Conference on Learning Representations, Vancouver, Canada, 2018.
[95]
Ho, J.; Jain, A.; Abbeel, P. Denoising diffusion probabilistic models. In Proceedings of the 34 th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, pp 574.
[96]

Bond-Taylor, S.; Leach, A.; Long, Y.; Willcocks, C. G. Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE Trans. Pattern Anal. Mach. Intell. 2022, 44, 7327–7347.
Crossref Google Scholar
[97]

Sui, C. X.; Li, Y. Y.; Li, X. Q.; Higueros, G.; Wang, K. Y.; Xie, W. R.; Hsu, P. C. Bio-inspired computational design of vascularized electrodes for high-performance fast-charging batteries optimized by deep learning. Adv. Energy Mater. 2022, 12, 2103044.
Crossref Google Scholar
[98]
Newman, J.; Balsara, N. P. Electrochemical Systems, 4th ed.; John Wiley & Sons: New York, 2021.
[99]

Li, L. S.; Erb, R. M.; Wang, J. J.; Wang, J.; Chiang, Y. M. Fabrication of low-tortuosity ultrahigh-area-capacity battery electrodes through magnetic alignment of emulsion-based slurries. Adv. Energy Mater. 2019, 9, 1802472.
Crossref Google Scholar
[100]

Wu, J. Y.; Ju, Z. Y.; Zhang, X.; Xu, X.; Takeuchi, K. J.; Marschilok, A. C.; Takeuchi, E. S.; Yu, G. H. Low-tortuosity thick electrodes with active materials gradient design for enhanced energy storage. ACS Nano 2022, 16, 4805–4812.
Crossref Google Scholar
[101]

Qi, Y. B.; Jang, T. J.; Ramadesigan, V.; Schwartz, D. T.; Subramanian, V. R. Is there a benefit in employing graded electrodes for lithium-ion batteries? J. Electrochem. Soc. 2017, 164, A3196–A3207.
Crossref Google Scholar
[102]

Ramadesigan, V.; Methekar, R. N.; Latinwo, F.; Braatz, R. D.; Subramanian, V. R. Optimal porosity distribution for minimized ohmic drop across a porous electrode. J. Electrochem. Soc. 2010, 157, A1328.
Crossref Google Scholar
[103]

Liu, L.; Guan, P. J.; Liu, C. H. Experimental and simulation investigations of porosity graded cathodes in mitigating battery degradation of high voltage lithium-ion batteries. J. Electrochem. Soc. 2017, 164, A3163–A3173.
Crossref Google Scholar
[104]

Zhang, X.; Hui, Z. Y.; King, S. T.; Wu, J. Y.; Ju, Z. Y.; Takeuchi, K. J.; Marschilok, A. C.; West, A. C.; Takeuchi, E. S.; Wang, L. et al. Gradient architecture design in scalable porous battery electrodes. Nano Lett. 2022, 22, 2521–2528.
Crossref Google Scholar
[105]

Huang, C.; Dontigny, M.; Zaghib, K.; Grant, P. S. Low-tortuosity and graded lithium ion battery cathodes by ice templating. J. Mater. Chem. A 2019, 7, 21421–21431.
Crossref Google Scholar
[106]

Kim, H.; Oh, S. K.; Lee, J.; Doo, S. W.; Kim, Y.; Lee, K. T. Failure mode of thick cathodes for Li-ion batteries: Variation of state-of-charge along the electrode thickness direction. Electrochim. Acta 2021, 370, 137743.
Crossref Google Scholar
[107]

Bae, C. J.; Erdonmez, C. K.; Halloran, J. W.; Chiang, Y. M. Design of battery electrodes with dual-scale porosity to minimize tortuosity and maximize performance. Adv. Mater. 2013, 25, 1254–1258.
Crossref Google Scholar
[108]

Sander, J. S.; Erb, R. M.; Li, L.; Gurijala, A.; Chiang, Y. M. High-performance battery electrodes via magnetic templating. Nat. Energy 2016, 1, 16099.
Crossref Google Scholar
[109]

Billaud, J.; Bouville, F.; Magrini, T.; Villevieille, C.; Studart, A. R. Magnetically aligned graphite electrodes for high-rate performance Li-ion batteries. Nat. Energy 2016, 1, 16097.
Crossref Google Scholar
[110]

Zhang, X.; Ju, Z. Y.; Housel, L. M.; Wang, L.; Zhu, Y.; Singh, G.; Sadique, N.; Takeuchi, K. J.; Takeuchi, E. S.; Marschilok, A. C. et al. Promoting transport kinetics in Li-ion battery with aligned porous electrode architectures. Nano Lett. 2019, 19, 8255–8261.
Crossref Google Scholar
[111]
Xu, T. Topology optimization of lithium ion batteries: How to maximize the discharge capacity by changing the geometry. Master thesis, Delft University of Technology, Delft, 2015.
[112]

Niu, Z. Q.; Zhao, W. H.; Wu, B.; Wang, H. Z.; Lin, W. F.; Pinfield, V. J.; Xuan, J. π Learning: A performance-informed framework for microstructural electrode design. Adv. Energy Mater. 2023, 13, 2300244.
Crossref Google Scholar
[113]

Arganda-Carreras, I.; Kaynig, V.; Rueden, C.; Eliceiri, K. W.; Schindelin, J.; Cardona, A.; Sebastian Seung, H. Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics 2017, 33, 2424–2426.
Crossref Google Scholar
[114]
Chan, H. P.; Samala, R. K.; Hadjiiski, L. M.; Zhou, C. Deep learning in medical image analysis. In Deep Learning in Medical Image Analysis: Challenges and Applications. Lee, G.; Fujita, H., Eds.; Springer: Cham, 2020; pp 3–21.
Crossref
[115]

Kench, S.; Cooper, S. J. Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. Nat. Mach. Intell. 2021, 3, 299–305.
Crossref Google Scholar
[116]

Xu, H. Y.; Zhu, J. E.; Finegan, D. P.; Zhao, H. B.; Lu, X. K.; Li, W.; Hoffman, N.; Bertei, A.; Shearing, P.; Bazant, M. Z. Guiding the design of heterogeneous electrode microstructures for Li-ion batteries: Microscopic imaging, predictive modeling, and machine learning. Adv. Energy Mater. 2021, 11, 2003908.
Crossref Google Scholar
[117]

Yang, Y. Z.; Li, N.; Wang, B.; Li, N.; Gao, K.; Liang, Y. D.; Wei, Y. M.; Yang, L.; Song, W. L.; Chen, H. S. Microstructure evolution of lithium-ion battery electrodes at different states of charge: Deep learning-based segmentation. Electrochem. Commun. 2022, 136, 107224.
Crossref Google Scholar
[118]

Gayon-Lombardo, A.; Mosser, L.; Brandon, N. P.; Cooper, S. J. Pores for thought: Generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries. npj Comput. Mater. 2020, 6, 82.
Crossref Google Scholar
[119]

Petrich, L.; Westhoff, D.; Feinauer, J.; Finegan, D. P.; Daemi, S. R.; Shearing, P. R.; Schmidt, V. Crack detection in lithium-ion cells using machine learning. Comput. Mater. Sci. 2017, 136, 297–305.
Crossref Google Scholar
[120]

Bedrov, D.; Piquemal, J. P.; Borodin, O.; Mackerell, A. D.; Roux, B.; Schröder, C. Molecular dynamics simulations of ionic liquids and electrolytes using polarizable force fields. Chem. Rev. 2019, 119, 7940–7995.
Crossref Google Scholar
[121]

Heid, E.; Fleck, M.; Chatterjee, P.; Schröder, C.; MacKerell, A. D. Jr. Toward prediction of electrostatic parameters for force fields that explicitly treat electronic polarization. J. Chem. Theory Comput. 2019, 15, 2460–2469.
Crossref Google Scholar
[122]

Schütt, K. T.; Sauceda, H. E.; Kindermans, P. J.; Tkatchenko, A.; Müller, K. R. SchNet—A deep learning architecture for molecules and materials. J. Chem. Phys. 2018, 148, 241722.
Crossref Google Scholar
[123]

Xie, T.; France-Lanord, A.; Wang, Y. M.; Shao-Horn, Y.; Grossman, J. C. Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials. Nat. Commun. 2019, 10, 2667.
Crossref Google Scholar
[124]

Xu, M. Y.; Zhu, T.; Zhang, J. Z. H. Molecular dynamics simulation of zinc ion in water with an ab initio based neural network potential. J. Phys. Chem A 2019, 123, 6587–6595.
Crossref Google Scholar
[125]

Nakayama, T.; Igarashi, Y.; Sodeyama, K.; Okada, M. Material search for Li-ion battery electrolytes through an exhaustive search with a Gaussian process. Chem. Phys. Lett. 2019, 731, 136622.
Crossref Google Scholar
[126]

Sodeyama, K.; Igarashi, Y.; Nakayama, T.; Tateyama, Y.; Okada, M. Liquid electrolyte informatics using an exhaustive search with linear regression. Phys. Chem. Chem. Phys. 2018, 20, 22585–22591.
Crossref Google Scholar
[127]

Dave, A.; Mitchell, J.; Kandasamy, K.; Wang, H.; Burke, S.; Paria, B.; Póczos, B.; Whitacre, J.; Viswanathan, V. Autonomous discovery of battery electrolytes with robotic experimentation and machine learning. Cell Rep. Phys. Sci. 2020, 1, 100264.
Crossref Google Scholar
[128]

Dave, A.; Mitchell, J.; Burke, S.; Lin, H. Y.; Whitacre, J.; Viswanathan, V. Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling. Nat. Commun. 2022, 13, 5454.
Crossref Google Scholar
[129]

Rupp, M.; Tkatchenko, A.; Müller, K. R.; von Lilienfeld, O. A. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 2012, 108, 058301.
Crossref Google Scholar
[130]

Li, K.; Wang, J. F.; Song, Y. Y.; Wang, Y. Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries. Nat. Commun. 2023, 14, 2789.
Crossref Google Scholar
[131]

Sendek, A. D.; Cubuk, E. D.; Antoniuk, E. R.; Cheon, G.; Cui, Y.; Reed, E. J. Machine learning-assisted discovery of solid Li-ion conducting materials. Chem. Mater. 2019, 31, 342–352.
Crossref Google Scholar
Nano Research Energy
Volume 3 Issue 2,
June 2024
Article number: e9120102
DOI: 10.26599/NRE.2023.9120102
Cite this article:
Sui C, Jiang Z, Higueros G, et al. Designing electrodes and electrolytes for batteries by leveraging deep learning. Nano Research Energy, 2024, 3: e9120102. https://doi.org/10.26599/NRE.2023.9120102

1978

Views

378

Downloads

1

Crossref

1

Scopus

Altmetrics
Received: 27 July 2023
Revised: 15 September 2023
Accepted: 17 September 2023
Published: 03 November 2023
© The Author(s) 2024. Published by Tsinghua University Press.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Return