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The extent of ion pairing in solution is an important phenomenon torationalise transport and thermodynamic properties of electrolytes. Afundamental measure of this pairing is the potential of mean force (PMF)between the solvated ions. The relative stabilities of the paired and solventseparated states in the PMF are highly sensitive to the underlying potentialenergy surface. However direct application of accurate electronic structuremethods to resolve this property is challenging, since long simulations arerequired. Leveraging developments in machine learning potentials and electronicstructure methods, we obtain wavefunction based models with RPA and MP2 for theprototypical system of Na and Cl ions in water. We show that even among thesemethods, discrepancies in the PMF still remain, and also highlight shortcomingsof density functional theory and classical force-field predictions. Thesemodels are primed for application to computationally intensive electrolyteproperties including transport coefficients and even confined systems, all ofwhich are highly sensitive to their chosen reference electronic structuremethod.

To pair or not to pair? Machine-learned explicitly-correlated electronic structure for NaCl in water