Rational electrolyte design for Li-metal batteries operated under extreme conditions: a combined DFT, COSMO-RS, and machine learning study

Developing electrolytes for Li-metal batteries capable of operating under extreme conditions is a significant challenge and is often hindered by the absence of systematic solvent screening studies. In this study, 190 binary mixtures comprising 20 solvents were assessed by calculating the density functional theory (DFT) and conductor-like screening model for realistic solvents (COSMO-RS) to identify electrolytes with a wide liquid temperature range and high LiTFSI solubility. Tetramethylene sulfone (TMS) has emerged as a promising candidate because of its high boiling point and low enthalpy of fusion, which increase the bubble point and reduce the eutectic temperature in mixtures. Utilizing a machine learning model with seven σ-descriptors, Li- and TFSI-ion binding energies were accurately predicted. These binding energies were primarily influenced by strong electrostatic and van der Waals interactions. This integrated approach highlights the effectiveness of the combined DFT, COSMO-RS, and machine learning techniques for guiding electrolyte design. We developed a computational protocol combining DFT, COSMO-RS, and machine learning to investigate the thermodynamic properties of 190 binary solvent mixtures. This approach demonstrates high potential for guiding electrolyte design.