Data-Driven Insights into Ionic Conductivity in High-Dimensional Sodium Battery Electrolytes

Abstract The discovery of advanced battery electrolytes is challenged by the vast compositional space of multi-component liquid formulations. Here, we introduce the ELectrolyte Laboratory for Integrated Experimentation (ELLIE), an automated platform that combines electrolyte formulation and impedance spectroscopy to map ionic conductivity across high-dimensional sodium electrolytes containing up to five salts and 15 solvents, generating an experimental dataset spanning nearly two orders of magnitude in conductivity. 23Na NMR, Raman spectroscopy, and viscosity measurements on a subset of electrolytes at a fixed salt concentration reveal that conductivity is jointly influenced by Na+ solvation strength, ion association, and solvent dynamics and positively correlates with inverse viscosity. Conductivity estimates based on the Nernst–Einstein relation captures broad concentration and viscosity relationships but do not extrapolate well across compositionally diverse electrolytes. Random forest modeling identifies lower solvent molecular weight as the dominant descriptor of high conductivity. Together, these results establish solvent molecular size as a physically interpretable descriptor of ion transport and demonstrate how automated experimentation can accelerate data-driven electrolyte optimization across complex compositional spaces.