Tabor, D. P. et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5–20 (2018).
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
Baumhöfer, T., Brühl, M., Rothgang, S. & Sauer, D. U. Production caused variation in capacity aging trend and correlation to initial cell performance. J. Power Sources 247, 332–338 (2014).
Keil, P. & Jossen, A. Charging protocols for lithium-ion batteries and their impact on cycle life—an experimental study with different 18650 high-power cells. J. Energy Storage 6, 125–141 (2016).
Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4, 383–391 (2019).
Schuster, S. F., Brand, M. J., Berg, P., Gleissenberger, M. & Jossen, A. Lithium-ion cell-to-cell variation during battery electric vehicle operation. J. Power Sources 297, 242–251 (2015).
Harris, S. J., Harris, D. J. & Li, C. Failure statistics for commercial lithium ion batteries: a study of 24 pouch cells. J. Power Sources 342, 589–597 (2017).
Ahmed, S. et al. Enabling fast charging—a battery technology gap assessment. J. Power Sources 367, 250–262 (2017).
Liu, Y., Zhu, Y. & Cui, Y. Challenges and opportunities towards fast-charging battery materials. Nat. Energy 4, 540–550 (2019).
Hoffman, M. W., Shahriari, B. & de Freitas, N. On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. In Proc. 17th Int. Conf. on Artificial Intelligence and Statistics (AISTATS) Vol. 33, 365–374 (Proceedings of Machine Learning Research, 2014); http://proceedings.mlr.press/v33/hoffman14.html.
Grover, A. et al. Best arm identification in multi-armed bandits with delayed feedback. In Proc. 21st Int. Conf. on Artificial Intelligence and Statistics (AISTATS) Vol. 84, 833–842 (Proceedings of Machine Learning Research, 2018); http://proceedings.mlr.press/v84/grover18b.html.
Nikolaev, P. et al. Autonomy in materials research: a case study in carbon nanotube growth. npj Comput. Mater. 2, 16031 (2016).
Ling, J., Hutchinson, M., Antono, E., Paradiso, S. & Meredig, B. High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates. Integr. Mater. Manuf. Innov. 6, 207–217 (2017).
Balachandran, P. V., Kowalski, B., Sehirlioglu, A. & Lookman, T. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat. Commun. 9, 1668 (2018).
Bédard, A.-C. et al. Reconfigurable system for automated optimization of diverse chemical reactions. Science 361, 1220–1225 (2018).
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 559, 377–381 (2018).
King, R. D. et al. The automation of science. Science 324, 85–89 (2009).
Schneider, G. Automating drug discovery. Nat. Rev. Drug Discov. 17, 97–113 (2018).
Domhan, T., Springenberg, J. T. & Hutter, F. Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In Proc. 24th Int. Conf. on Artificial Intelligence 3460–3468 (AAAI Press, 2015).
Klein, A., Falkner, S., Springenberg, J. T. & Hutter, F. Learning curve prediction with Bayesian neural networks. In Proc. 2017 Int. Conf. on Learning Representations 1–16 (2017); https://openreview.net/forum?id=S11KBYclx.
Petrak, J. Fast Subsampling Performance Estimates for Classification Algorithm Selection. Technical Report TR-2000-07, 3–14 (Austrian Research Institute for Artificial Intelligence, 2000); http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.28.3305&rep=rep1&type=pdf.
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. & Talwalkar, A. Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18, 1–52 (2018).
Hutter, F., Hoos, H. H. & Leyton-Brown, K. Sequential model-based optimization for general algorithm configuration. In Proc. 5th Int. Conf. on Learning and Intelligent Optimization 507–523 (Springer, 2011).
Luo, Y., Liu, Y. & Wang, S. Search for an optimal multistage charging pattern for lithium-ion batteries using the Taguchi approach. In Region 10 Conf. (TENCON 2009) 1–5, https://doi.org/10.1109/TENCON.2009.5395823 (IEEE, 2009).
Liu, Y., Hsieh, C. & Luo, Y. Search for an optimal five-step charging pattern for Li-ion batteries using consecutive orthogonal arrays. IEEE Trans. Energ. Convers. 26, 654–661 (2011).
Schindler, S., Bauer, M., Cheetamun, H. & Danzer, M. A. Fast charging of lithium-ion cells: identification of aging-minimal current profiles using a design of experiment approach and a mechanistic degradation analysis. J. Energy Storage 19, 364–378 (2018).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B 67, 301–320 (2005).
Keil, P. et al. Calendar aging of lithium-ion batteries. I. Impact of the graphite anode on capacity fade. J. Electrochem. Soc. 163, A1872–A1880 (2016).
Wood, D. L., Li, J. & Daniel, C. Prospects for reducing the processing cost of lithium ion batteries. J. Power Sources 275, 234–242 (2015).
Zimmerman, A. H., Quinzio, M. V. & Monica, S. Adaptive charging method for lithium-ion battery cells. US Patent US6204634B1 (2001).
Park, S., Kato, D., Gima, Z., Klein, R. & Moura, S. Optimal experimental design for parameterization of an electrochemical lithium-ion battery model. J. Electrochem. Soc. 165, A1309–A1323 (2018).
Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O. & Roitberg, A. E. Less is more: sampling chemical space with active learning. J. Chem. Phys. 148, 241733 (2018).
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016).
Audibert, J.-Y., Bubeck, S. & Munos, R. Best arm identification in multi-armed bandits. In Proc. 23rd Conf. on Learning Theory (COLT) 41–53 (2010); http://certis.enpc.fr/~audibert/Mes%20articles/COLT10.pdf.
Srinivas, N., Krause, A., Kakade, S. M. & Seeger, M. W. Information-theoretic regret bounds for Gaussian process optimization in the bandit setting. IEEE Trans. Inf. Theory 58, 3250–3265 (2012).
Drake, S. J. et al. Measurement of anisotropic thermophysical properties of cylindrical Li-ion cells. J. Power Sources 252, 298–304 (2014).
Çengel, Y. A. & Boles, M. A. Thermodynamics: An Engineering Approach (McGraw-Hill Education, 2015).
Smith, A. J., Burns, J. C., Zhao, X., Xiong, D. & Dahn, J. R. A high precision coulometry study of the SEI growth in Li/graphite cells. J. Electrochem. Soc. 158, A447–A452 (2011).
Zhang, S. S. The effect of the charging protocol on the cycle life of a Li-ion battery. J. Power Sources 161, 1385–1391 (2006).
Kim, J. M. et al. Battery charging method and battery pack using the same. US Patent Application US20160226270A1 (2016).
Lee, M.-S., Song, S.-B., Jung, J.-S. & Golovanov, D. Battery charging method and battery pack using the same. US Patent US9917458B2 (2018).
Notten, P. H. L., Op het Veld, J. H. G. & van Beek, J. R. G. Boostcharging Li-ion batteries: a challenging new charging concept. J. Power Sources 145, 89–94 (2005).
Paryani, A. Low temperature charging of Li-ion cells. US Patent US8552693B2 (2013).
Mehta, V. H. & Straubel, J. B. Fast charging with negative ramped current profile. US Patent US8643342B2 (2014).
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