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We obtain activity coefficients and solubilities of NaCl in water-methanol solutions at 298.15 K and 1 bar from molecular dynamics (MD) simulations with the Joung-Cheatham, SPC/E, and OPLS-AA force fields for NaCl, water, and methanol, respectively. The Lorentz-Berthelot combining rules were adopted for the unlike-pair interactions of Na

The dielectric permittivity of salt water decreases on dissolving more salt. For nearly a century, this phenomenon has been explained by invoking saturation in the dielectric response of the solvent water molecules. Herein, we employ an advanced deep neural network (DNN), built using data from density functional theory, to study the dielectric permittivity of sodium chloride solutions. Notably, the decrease in the dielectric permittivity as a function of concentration, computed using the DNN approach, agrees well with experiments. Detailed analysis of the computations reveals that the dominant effect, caused by the intrusion of ionic hydration shells into the solvent hydrogen-bond network, is the disruption of dipolar correlations among water molecules. Accordingly, the observed decrease in the dielectric permittivity is mostly due to increasing suppression of the collective response of solvent waters., Comment: has accepted by Physical Review Letters

The formation of ice in the atmosphere affects precipitation and cloud properties, and plays a key role in the climate of our planet. Although ice can form directly from liquid water at deeply supercooled conditions, the presence of foreign particles can aid ice formation at much warmer temperatures. Over the past decade, experiments have highlighted the remarkable efficiency of feldspar minerals as ice nuclei compared to other particles present in the atmosphere. However, the exact mechanism of ice formation on feldspar surfaces has yet to be fully understood. Here, we develop a first-principles machine-learning model for the potential energy surface aimed at studying ice nucleation at microcline feldspar surfaces. The model is able to reproduce with high fidelity the energies and forces derived from density-functional theory (DFT) based on the SCAN exchange and correlation functional. We apply the machine-learning force field to study different fully-hydroxylated terminations of the (100), (010), and (001) surfaces of microcline exposed to vacuum. Our calculations suggest that terminations that do not minimize the number of broken bonds are preferred in vacuum. We also study the structure of supercooled liquid water in contact with microcline surfaces, and find that water density correlations extend up to around 1 nm from the surfaces. Finally, we show that the force field maintains a high accuracy during the simulation of ice formation at microcline surfaces, even for large systems of around 30,000 atoms. Future work will be directed towards the calculation of nucleation free energy barriers and rates using the force field developed herein, and understanding the role of different microcline surfaces on ice nucleation., Comment: 12 pages, 6 figures

Colloidal particles, amphiphiles, and functionalized nanoparticles are examples of systems that frequently exhibit short-range attractions coupled with long-range repulsions. In this work, we observe striking differences in the dynamics of self-assembled clusters that form in a simple isotropic model of such systems when the strength of attraction is varied. We find that while attraction-dominated particles self-assemble into compact clusters with properties similar to micelles formed by amphiphilic molecules, repulsion-dominated particles self-assemble into open clusters which have much shorter life-times. There is also a significantly different dependence of the solution osmotic pressure versus composition: formation of compact clusters causes a decrease in the pressure vs. density slope, while formation of open clusters does not effect the pressure. This thermodynamic quantity turns out to be much more sensitive in picking out different clustering characteristics than the overall aggregation curves or cluster shapes. Our results have significant implications in developing design principles for stable cluster self-assembly and detection in both laboratory settings and in computer simulations., Comment: The cluster life-times for the attraction-dominated (epsilon = 1.6) case, shown as the red curve in Fig. 4, was previously at the incorrect temperature. Previously the temperature was kT = 0.67, in the new version the temperature is kT = 0.75, as the figure label states. The text is altered accordingly

We discuss the detailed balance condition for hybrid Monte Carlo method

The behavior of confined cylindrical micelle-forming surfactants under the influence of shear has been investigated using Monte Carlo simulations. The surfactants are modeled as coarse-grained lattice polymers, while the Monte Carlo shear flow is implemented with an externally imposed potential energy field which induces a linear drag velocity on the surfactants. It is shown that in the absence of shear, cylindrical micelles confined within a monolayer coarsen gradually with Monte Carlo "time" t, the persistence length of the micelles scaling as t^{0.24}, in agreement with the scaling obtained experimentally. Under the imposition of shear, the micelles within a monolayer align parallel to the direction of shear, as observed experimentally. Micelles confined within thicker films also align parallel to each other with a hexagonal packing under shear, but assume a finite tilt with respect to the velocity vector within the velocity-velocity gradient plane. We propose a novel mechanism for this shear-induced alignment of micelles based on breaking up of micelles aligned perpendicular to shear and their reformation and subsequent growth in the shear direction. It is observed that there exists a "window" of shear rates within which such alignment occurs. A phenomenological theory proposed to explain the above behavior is in good agreement with simulation results. A comparison of simulated and experimental self-diffusivities yields a physical timescale for Monte Carlo moves, which enables an assessment of the physical shear rates employed in our Monte Carlo simulations., 23 pages, 8 figures

The phase behavior of the lattice restricted primitive model (RPM) for ionic systems with additional short-range nearest neighbor (nn) repulsive interactions has been studied by grand canonical Monte Carlo simulations. We obtain a rich phase behavior as the nn strength is varied. In particular, the phase diagram is very similar to the continuum RPM model for high nn strength. Specifically, we have found both gas-liquid phase separation, with associated Ising critical point, and first-order liquid-solid transition. We discuss how the line of continuous order-disorder transitions present for the low nn strength changes into the continuum-space behavior as one increases the nn strength and compare our findings with recent theoretical results by Ciach and Stell [Phys. Rev. Lett. {\bf 91}, 060601 (2003)]., Comment: 7 pages, 10 figures