Data-Driven Analysis of High-Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity

An in-house, unique, custom-developed high-throughput experimentation facility, used for discovery of novel and optimization of existing electrolyte formulations for diverse cell chemistries and targeted applications, follows a high-throughput formulation-characterization-performance-elucidation-optimization-evaluation chain based on a set of previously established requirements. Here, we propose a scalable data-driven workflow to predict ionic conductivities of non-aqueous battery electrolytes based on linear and Gaussian regression, considering a dataset acquired from one-of-a-kind high-throughput electrolyte formulation to high-throughput conductivity measurement sequence. Deeper insight into various compositional effects is gained from a generalized Arrhenius analysis, in which conductivities, activation energies and deviations from Arrhenius behavior are determined separately. Each observable displays a specific dependence on the electrolyte salt concentration. The conductivity is fully insensitive to the addition of electrolyte additives for otherwise constant molar composition. We also discuss and interpret qualitative trends predicted by the data-driven model in light of physical features such as viscosity or ion association effects.