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

The extent of ion pairing in solution is an important phenomenon to rationalise transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the potential of mean force (PMF) between the solvated ions. The relative stabilities of the paired and solvent separated states in the PMF are highly sensitive to the underlying potential energy surface. However direct application of accurate electronic structure methods to resolve this property is challenging, since long simulations are required. Leveraging developments in machine learning potentials and electronic structure methods, we obtain wavefunction based models with RPA and MP2 for the prototypical system of Na and Cl ions in water. We show that even among these methods, discrepancies in the PMF still remain, and also highlight shortcomings of density functional theory and classical force-field predictions. These models are primed for application to computationally intensive electrolyte properties including transport coefficients and even confined systems, all of which are highly sensitive to their chosen reference electronic structure method