To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water

Abstract

The extent of ion pairing in solution is an important phenomenon to rationalize transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the potential of mean force (PMF) between solvated ions. The relative stabilities of the paired and solvent shared states in the PMF and the barrier between them are highly sensitive to the underlying potential energy surface. However, direct application of accurate electronic structure methods is challenging, since long simulations are required. We develop wave function based machine learning potentials with the random phase approximation (RPA) and second order Møller–Plesset (MP2) perturbation theory for the prototypical system of Na and Cl ions in water. We show both methods in agreement, predicting the paired and solvent shared states to have similar energies (within 0.2 kcal/mol). We also provide the same benchmarks for different DFT functionals as well as insight into the PMF based on simple analyses of the interactions in the system.

PW-SMD: A Plane-Wave Implicit Solvation Model Based on Electron Density for Surface Chemistry and Crystalline Systems in Aqueous Solution

“…Thus, the all-atom explicit-solvent simulation may not yield better solvation free energies compared with a well-developed implicit solvation model. Nevertheless, as evidenced by recent efforts in simulating solvation dynamics through machine-learned potentials, − with extensive training of neural-network potentials on the electronic potential-energy surface (PES) and its gradients, we are able to perform neural-network PES-based explicit-solvent simulations at a much better level of accuracy.…”