Ion transport through electrolytes critically impacts
the performance
of batteries and other devices. Many frameworks used to model ion
transport assume hydrodynamic mechanisms and focus on maximizing conductivity
by minimizing viscosity. However, solid-state electrolytes illustrate
that non-hydrodynamic ion transport can define device performance.
Increasingly, selective transport mechanisms, such as hopping, are
proposed for concentrated electrolytes. However, viscosity–conductivity
scaling relationships in ionic liquids are often analyzed with hydrodynamic
models. We report data-centric analyses of hydrodynamic transport
models of viscosity–conductivity scaling in ionic liquids by
merging three databases to bridge physical properties and computational
descriptors. With this expansive database, we constrained scaling
analyses using ion sizes defined from simulated volumes, as opposed
to estimating sizes from activity coefficients. Remarkably, we find
that many ionic liquids exhibit positive deviations from the Nernst–Einstein
model, implying ions move faster than hydrodynamics should allow.
We verify these findings using microrheology and conductivity experiments.
We further show that machine learning tools can improve predictions
of conductivity from molecular properties, including predictions from
solely computational features. Our findings reveal that many ionic
liquids exhibit super-hydrodynamic viscosity–conductivity scaling,
suggesting mechanisms of correlated ion motion, which could be harnessed
to enhance electrochemical device performance.