Amici, J. et al. A roadmap for transforming research to invent the batteries of the future designed within the european large scale research initiative battery 2030+. Advanced energy materials 12, 2102785 (2022).ArticleÂ
CASÂ
Google ScholarÂ
Park, M., Zhang, X., Chung, M., Less, G. B. & Sastry, A. M. A review of conduction phenomena in li-ion batteries. Journal of power sources 195, 7904–7929 (2010).ArticleÂ
CASÂ
Google ScholarÂ
Heubner, C., Schneider, M. & Michaelis, A. Diffusion-limited c-rate: a fundamental principle quantifying the intrinsic limits of li-ion batteries. Advanced Energy Materials 10, 1902523 (2020).ArticleÂ
CASÂ
Google ScholarÂ
Xu, K. Nonaqueous liquid electrolytes for lithium-based rechargeable batteries. Chemical reviews 104, 4303–4418 (2004).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Dave, A. et al. Autonomous optimization of non-aqueous li-ion battery electrolytes via robotic experimentation and machine learning coupling. Nature communications 13, 5454 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Vogler, M. et al. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647–2665 (2023).ArticleÂ
Google ScholarÂ
Kontogeorgis, G. M., Maribo-Mogensen, B. & Thomsen, K. The debye-hückel theory and its importance in modeling electrolyte solutions. Fluid Phase Equilibria 462, 130–152 (2018).ArticleÂ
CASÂ
Google ScholarÂ
Bedrov, D. et al. Molecular dynamics simulations of ionic liquids and electrolytes using polarizable force fields. Chemical reviews 119, 7940–7995 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Smith, R. B. & Bazant, M. Z. Multiphase porous electrode theory. Journal of The Electrochemical Society 164, E3291 (2017).ArticleÂ
CASÂ
Google ScholarÂ
Latz, A. & Zausch, J. Thermodynamic consistent transport theory of li-ion batteries. Journal of Power Sources 196, 3296–3302 (2011).ArticleÂ
CASÂ
Google ScholarÂ
Nilsson-Hallén, J., Ahlström, B., Marczewski, M. & Johansson, P. Ionic liquids: A simple model to predict ion conductivity based on dft derived physical parameters. Frontiers in chemistry 7, 126 (2019).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Landesfeind, J. & Gasteiger, H. A. Temperature and concentration dependence of the ionic transport properties of lithium-ion battery electrolytes. Journal of The Electrochemical Society 166, A3079–A3097 (2019).ArticleÂ
Google ScholarÂ
Flores, E. et al. Learning the laws of lithium-ion transport in electrolytes using symbolic regression. Digital Discovery 1, 440–447 (2022).ArticleÂ
CASÂ
Google ScholarÂ
Datta, R., Ramprasad, R. & Venkatram, S. Conductivity prediction model for ionic liquids using machine learning. The Journal of Chemical Physics156 (2022).Rahmanian, F. et al. One-shot active learning for globally optimal battery electrolyte conductivity. Batteries & Supercaps 5, e202200228 (2022).ArticleÂ
Google ScholarÂ
Krishnamoorthy, A. N. et al. Data-driven analysis of high-throughput experiments on liquid battery electrolyte formulations: Unraveling the impact of composition on conductivity (2022).Rahmanian, F. et al. Conductivity experiments for electrolyte formulations and their automated analysis. Scientific Data 10, 43 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Dos Reis, G., Strange, C., Yadav, M. & Li, S. Lithium-ion battery data and where to find it. Energy and AI 5, 100081 (2021).ArticleÂ
Google ScholarÂ
Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy 4, 383–391 (2019).ArticleÂ
Google ScholarÂ
Hu, X., Xu, L., Lin, X. & Pecht, M. Battery lifetime prognostics. Joule 4, 310–346 (2020).ArticleÂ
CASÂ
Google ScholarÂ
Rieger, L. H. et al. Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory. Digital Discovery 2, 112–122 (2023).ArticleÂ
CASÂ
Google ScholarÂ
Castelli, I. E. et al. Data management plans: the importance of data management in the big-map project. Batteries & Supercaps 4, 1803–1812 (2021).ArticleÂ
Google ScholarÂ
Ding, M. S., Xu, K. & Jow, T. R. Conductivity and Viscosity of PC-DEC and PC-EC Solutions of LiBOB. Journal of The Electrochemical Society 152, A132 (2004).ArticleÂ
Google ScholarÂ
Ding, M. S. Electrolytic conductivity and glass transition temperature as functions of salt content, solvent composition, or temperature for LiPF6 in propylene carbonate+ diethyl carbonate. Journal of Chemical & Engineering Data 48, 519–528 (2003).ArticleÂ
CASÂ
Google ScholarÂ
Dudley, J. et al. Conductivity of electrolytes for rechargeable lithium batteries. Journal of power sources 35, 59–82 (1991).ArticleÂ
CASÂ
Google ScholarÂ
Ding, M. S. Conductivity and viscosity of PC-DEC and PC-EC solutions of LiBF4. Journal of the Electrochemical Society 151, A40 (2003).ArticleÂ
Google ScholarÂ
Ding, M. S. Electrolytic conductivity and glass transition temperatures as functions of salt content, solvent composition, or temperature for LiBF4 in propylene carbonate+ diethyl carbonate. Journal of Chemical & Engineering Data 49, 1102–1109 (2004).ArticleÂ
CASÂ
Google ScholarÂ
Ding, M. S. & Jow, T. R. Conductivity and viscosity of PC-DEC and PC-EC solutions of LiPF6. Journal of the Electrochemical Society 150, A620 (2003).ArticleÂ
CASÂ
Google ScholarÂ
Ding, M. S. & Jow, T. R. Properties of PC-EA solvent and its solution of LiBOB comparison of linear esters to linear carbonates for use in lithium batteries. Journal of the electrochemical society 152, A1199 (2005).ArticleÂ
CASÂ
Google ScholarÂ
Ding, M. S., Xu, K. & Jow, T. R. Effects of tris (2, 2, 2-trifluoroethyl) phosphate as a flame-retarding cosolvent on physicochemical properties of electrolytes of LiPF6 in EC-PC-EMC of 3: 3: 4 weight ratios. Journal of The Electrochemical Society 149, A1489 (2002).ArticleÂ
CASÂ
Google ScholarÂ
Xu, W. et al. Structures of orthoborate anions and physical properties of their lithium salt nonaqueous solutions. Journal of the Electrochemical Society 150, E74 (2002).ArticleÂ
Google ScholarÂ
Xu, W. & Angell, C. A. Weakly coordinating anions, and the exceptional conductivity of their nonaqueous solutions. Electrochemical and Solid-State Letters 4, E1 (2000).ArticleÂ
Google ScholarÂ
Ding, M. et al. Change of conductivity with salt content, solvent composition, and temperature for electrolytes of LiPF6 in ethylene carbonate-ethyl methyl carbonate. Journal of the Electrochemical Society 148, A1196 (2001).ArticleÂ
CASÂ
Google ScholarÂ
Jow, T. et al. Nonaqueous electrolytes for wide-temperature-range operation of Li-ion cells. Journal of Power Sources 119, 343–348 (2003).ArticleÂ
Google ScholarÂ
Logan, E. et al. A study of the physical properties of Li-ion battery electrolytes containing esters. Journal of The Electrochemical Society 165, A21 (2018).ArticleÂ
CASÂ
Google ScholarÂ
Logan, E. et al. A study of the transport properties of ethylene carbonate-free Li electrolytes. Journal of The Electrochemical Society 165, A705–A716 (2018).ArticleÂ
CASÂ
Google ScholarÂ
Dahbi, M., Ghamouss, F., Tran-Van, F., Lemordant, D. & Anouti, M. Comparative study of EC/DMC LiTFSI and LiPF6 electrolytes for electrochemical storage. Journal of Power Sources 196, 9743–9750 (2011).ArticleÂ
CASÂ
Google ScholarÂ
Geoffroy, I., Willmann, P., Mesfar, K., Carré, B. & Lemordant, D. Electrolytic characteristics of ethylene carbonate–diglyme-based electrolytes for lithium batteries. Electrochimica acta 45, 2019–2027 (2000).ArticleÂ
CASÂ
Google ScholarÂ
Han, H.-B. et al. Lithium bis (fluorosulfonyl) imide (LiFSI) as conducting salt for nonaqueous liquid electrolytes for lithium-ion batteries: Physicochemical and electrochemical properties. Journal of Power Sources 196, 3623–3632 (2011).ArticleÂ
CASÂ
Google ScholarÂ
Zhang, S., Tsuboi, A., Nakata, H. & Ishikawa, T. Database and models of electrolyte solutions for lithium battery. Journal of power sources 97, 584–588 (2001).ArticleÂ
Google ScholarÂ
Niedzicki, L. et al. New covalent salts of the 4+ V class for Li batteries. Journal of Power Sources 196, 8696–8700 (2011).ArticleÂ
CASÂ
Google ScholarÂ
Valøen, L. O. & Reimers, J. N. Transport properties of LiPF6-based Li-ion battery electrolytes. Journal of The Electrochemical Society 152, A882 (2005).ArticleÂ
Google ScholarÂ
Gu, G., Laura, R. & Abraham, K. Conductivity-Temperature Behavior of Organic Electrolytes. Electrochemical and solid-state letters 2, 486 (1999).ArticleÂ
CASÂ
Google ScholarÂ
Zhang, S. S., Xu, K. & Jow, T. R. Study of LiBF4 as an electrolyte salt for a Li-ion battery. Journal of the Electrochemical Society 149, A586 (2002).ArticleÂ
CASÂ
Google ScholarÂ
Zhang, S., Xu, K. & Jow, T. Low-temperature performance of Li-ion cells with a LiBF 4-based electrolyte. Journal of Solid State Electrochemistry 7, 147–151 (2003).ArticleÂ
CASÂ
Google ScholarÂ
Kim, H.-S. & Jeong, C.-S. Electrochemical properties of binary electrolytes for lithium-sulfur batteries. Bulletin of the Korean Chemical Society 32, 3682–3686 (2011).ArticleÂ
CASÂ
Google ScholarÂ
Lundgren, H., Behm, M. & Lindbergh, G. Electrochemical characterization and temperature dependency of mass-transport properties of LiPF6 in EC: DEC. Journal of The Electrochemical Society 162, A413 (2014).ArticleÂ
Google ScholarÂ
Nyman, A., Behm, M. & Lindbergh, G. Electrochemical characterisation and modelling of the mass transport phenomena in LiPF6–EC–EMC electrolyte. Electrochimica Acta 53, 6356–6365 (2008).ArticleÂ
CASÂ
Google ScholarÂ
Rohatgi, A. Webplotdigitizer: Version 4.6. https://automeris.io/WebPlotDigitizer (2022).pandas development team, T. pandas-dev/pandas: Pandas. Zenodo https://doi.org/10.5281/zenodo.3509134 (2020).McKinney, W. Data Structures for Statistical Computing in Python. In van der Walt, Stéfan & Millman, J. (eds.) Proceedings of the 9th Python in Science Conference, 56–61, https://doi.org/10.25080/Majora-92bf1922-00a (2010).Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hunter, J. D. Matplotlib: A 2D graphics environment. Computing in Science & Engineering 9, 90–95, https://doi.org/10.1109/MCSE.2007.55 (2007).ArticleÂ
Google ScholarÂ
de Blasio, P. V. F., Elsborg, J. T., Vegge, T., Flores, E. & Bhowmik, A. CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations. DTU Data https://doi.org/10.11583/DTU.c.6929599.v1 (2023).de Blasio, P. Code for converting CALiSol-23, https://github.com/Pele0599/CALiSol-23 (2023).Pesaran, A., Santhanagopalan, S. & Kim, G. Addressing the impact of temperature extremes on large format li-ion batteries for vehicle applications (presentation). Tech. Rep., National Renewable Energy Lab. (NREL), Golden, CO (United States) (2013).Bradford, G. et al. Chemistry-informed machine learning for polymer electrolyte discovery. ACS Central Science 9, 206–216 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ranković, B., Griffiths, R.-R., Moss, H. B. & Schwaller, P. Bayesian optimisation for additive screening and yield improvements–beyond one-hot encoding. Digital Discovery 3, 654–666 (2024).ArticleÂ
Google ScholarÂ
Jirasek, F. & Hasse, H. Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures. Annual Review of Chemical and Biomolecular Engineering 14, 31–51 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wigh, D. S., Goodman, J. M. & Lapkin, A. A. A review of molecular representation in the age of machine learning. Wiley Interdisciplinary Reviews: Computational Molecular Science 12, e1603 (2022).
Google ScholarÂ
Musil, F. et al. Physics-inspired structural representations for molecules and materials. Chemical Reviews 121, 9759–9815 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ajmani, S., Rogers, S. C., Barley, M. H. & Livingstone, D. J. Application of qspr to mixtures. Journal of chemical information and modeling 46, 2043–2055 (2006).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Oprisiu, I. et al. Qspr approach to predict nonadditive properties of mixtures. application to bubble point temperatures of binary mixtures of liquids. Molecular Informatics 31, 491–502 (2012).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Sharma, V. et al. Formulation graphs for mapping structure-composition of battery electrolytes to device performance. Journal of Chemical Information and Modeling 63, 6998–7010 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kuzhagaliyeva, N., Horváth, S., Williams, J., Nicolle, A. & Sarathy, S. M. Artificial intelligence-driven design of fuel mixtures. Communications Chemistry 5, 111 (2022).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hanaoka, K. Deep neural networks for multicomponent molecular systems. ACS omega 5, 21042–21053 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Dobbelaere, M. R. et al. Machine learning for physicochemical property prediction of complex hydrocarbon mixtures. Industrial & Engineering Chemistry Research 61, 8581–8594 (2022).ArticleÂ
CASÂ
Google ScholarÂ
Tian, Y., Wang, X., Liu, Y. & Hu, W. Prediction of co2 and n2 solubility in ionic liquids using a combination of ionic fragments contribution and machine learning methods. Journal of Molecular Liquids 383, 122066 (2023).ArticleÂ
CASÂ
Google ScholarÂ