Mean ionic activity coefficients in aqueous NaCl solutions from molecular dynamics simulations

Abstract

The mean ionic activity coefficients of aqueous NaCl solutions of varying concentrations at 298.15 K and 1 bar have been obtained from molecular dynamics simulations by gradually turning on the interactions of an ion pair inserted into the solution. Several common non-polarizable water and ion models have been used in the simulations. Gibbs-Duhem equation calculations of the thermodynamic activity of water are used to confirm the thermodynamic consistency of the mean ionic activity coefficients. While the majority of model combinations predict the correct trends in mean ionic activity coefficients, they overestimate their values at high salt concentrations. The solubility predictions also suffer from inaccuracies, with all models underpredicting the experimental values, some by large factors. These results point to the need for further ion and water model development.

Osmotic pressure of aqueous electrolyte solutions via molecular simulations of chemical potentials: Application to NaCl

“…The larger uncertainty bars on the AH/SWM4-DP and AH/BK3 curves are due to the much more time-consuming nature of the calculation in comparison with the case of non-polarizable force fields, resulting in reduced precision for a given computation time. Also shown for comparison for the JC FF are simulation data points of Mester and Panagiotopoulos [38], obtained using a gradual particle insertion method in conjunction with MD simulations; these results are seen to be in mutual agreement with our results.…”

“…To our knowledge, this was the first such use of this consistency test in conjunction with simulations, and it provided support for the correctness of our calculation procedures. This is further supported by the mutual agreement of our Osmotic Ensemble Monte Carlo (OEMC) results in Table 1 and the recent calculations of Mester and Panagiotopoulos [38], who used a different methodology.…”

“…MD with slab geometry [23] 7.27(7) MC and thermodynamic integration [4] 4.8(3) MD with slab geometry [4] 5.5(4) OEMC [44,45] 3.64(20) MD with slab geometry [27] 6.20 MD and thermodynamic integration [27] 6.42 MD with MC particle insertion [38] 3.59(4) Experiment [18] 6.14 in the presence of solid phases (for example, a "slab configuration" with the solution phase located between two crystalline solid phases), and solubility is determined as the composition of the electrolyte in the solution phase from a sufficiently long simulation run [23,4,13,27]. Another MD-based methodology seeks to determine the solubility by examining the composition at which sufficiently large ion clusters emerge in the course of the simulations [3,2,37].…”

“…The calculation of solubility for aqueous electrolytes provides a particularly interesting example of the different MD and MC approaches used. All MC methods determine the solubility by directly calculating the composition at which the electrolyte solution and crystalline solid chemical potentials are equal [15,33,55,42,4,49,45,44,38], whereas MD methodologies determine the solubility indirectly. One MD approach implements a physical configuration in which the solution phase is equilibrated Table 1 Aqueous solubility simulation results by various research groups for NaCl at T = 298.15 K and P = 1 bar using the Joung-Cheatham SPC/E-compatible FF [22].…”

Computational methodology for solubility prediction: Application to the sparingly soluble solutes

“…[14][15][16][17][18][19][20] The alternative "thermodynamic" approach determines the saturation solute composition by imposing the chemical potential of the solid phase to the solution in a grand canonical style. Such a route has been followed in the osmotic ensemble method/OEMC/OEMD.…”