Data-Driven Analysis of High-Throughput Experiments on Liquid Battery Electrolyte Formulations

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In all types of past, current and future batteries, electrolytes and their ad hoc interfacial/interphasial chemistries play a central role in terms of design and control of the electrode processes, material interactions, overall performance, long-term stability, safety and cost of a battery. [1-3] However, the complex nature of the electrolyte and the different structure-performance-relation remain an area that has not been fully discovered. For this reason, there is an urgent need to explore the electrolyte frontier and push our current understanding of electrolyte (electro)-chemistry to advance future battery development.

In this work, we introduce High-Throughput Experimentation (HTE) facility combined with data-driven analysis to enable time-efficient strategies for optimization of ionic conductivity of liquid battery electrolytes. HT experiments provide an autonomous tool to generate vast datasets, from which useful knowledge, underlying scientific phenomena and target entries can be obtained using data-driven tools and algorithms. A developed data-driven model by using a generalized Arrhenius fit and thereafter Linear Regression (LR) and Gaussian Process Regression (GPR) enabled to find individual terms that correlate the electrolyte composition to the determined conductivity for selected electrolyte compositions and considered temperatures. Surrogate models predict ionic conductivities with high accuracy and provide reliable estimates at very low cost compared to actual experiments. Also, the transformed data-driven model provides the feature trends of individual electrolyte compositions on ionic conductivity. All data were extracted from HT experiments with conducting salt, solvent/co-solvent, additive compositions (X (x1, x2, x3)) and temperature (T) as features and ionic conductivity as target quantity. [4] In this case, 1200 experimentally acquired data points, were utilized to analyze and predict ionic conductivities of liquid electrolyte formulations.

1 M. Winter, B. Barnett, K. Xu, Chem. Rev. 2018, 118, 11433–11456.
2 M. Gauthier, T. J. Carney, A. Grimaud, L. Giordano, N. Pour, H.-H. Chang, D. P. Fenning, S. F. Lux, O. Paschos, C. Bauer, F. Maglia, S. Lupart, P. Lamp, Y. Shao-Horn, J. Phys. Chem. Lett. 2015, 6, 4653–4672.
3 N. Aspern, G. ‐V. Röschenthaler, M. Winter, I. Cekic‐Laskovic, Angew. Chem. Int. Ed. 2019, 58, 15978–16000.
4 A. Benayad, D. Diddens, A. Heuer, A. N. Krishnamoorthy, M. Maiti, F. L. Cras, M. Legallais, F. Rahmanian, Y. Shin, H. Stein, M. Winter, C. Wölke, P. Yan, I. Cekic-Laskovic, Adv. Energy Mater. 2021, 2102678.

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