Multiscale Modeling of Ion Transport in Nanopores: Fitting Implicit-Water Radial Diffusion Profiles to Explicit-Water Molecular Dynamics
We develop a multiscale approach for incorporating molecular-scale transport information into computationally efficient models of ion transport through nanopores. A radially varying effective diffusion coefficient profile is fitted to radial conductivity profiles obtained from explicit-water molecular dynamics (MD) simulations. The fitting is performed within the NP+LEMC framework, which combines the Nernst--Planck equation with Local Equilibrium Monte Carlo to account for ion correlations beyond mean-field approximation. We apply the approach to NaCl, CaCl$_2$, and their mixtures in a negatively charged silica nanopore. The resulting diffusion coefficient profiles reproduce the radial current distributions of the MD simulations, including the strong suppression of ionic mobility near the pore wall that cannot be captured by a spatially constant diffusion coefficient inside the pore. NaCl and CaCl$_2$ exhibit qualitatively different transport behavior: the former is cation selective due to enhanced near-wall Na$^+$ conduction, whereas the latter shows weak anion selectivity because strongly bound Ca$^{2+}$ ions have strongly suppressed mobility near the surface. For NaCl--CaCl$_2$ mixtures, preferential Ca$^{2+}$ binding leads to nonlinear changes in ionic conductance. The approach establishes a bridge between experimentally relevant device behavior and computationally efficient reduced models, with explicit-water MD providing the molecular-scale information that can be incorporated into the effective transport coefficients.