The electrochemical surface potential across the water-vapor interface provides a measure of the orientation of water molecules at the interface. However, the large discrepancies between surface potentials calculated from ab initio (AI) and classical molecular dynamics (MD) simulations indicate that what is being calculated may be relevant to different test probes. Although a method for extracting the electrochemical surface potential from AIMD simulations has been given, methods for MD simulations have not been clarified. Here, two methods for extracting the surface potential relevant to electrochemical measurements from MD simulations are presented. This potential is shown to be almost entirely due to the dipole contribution. In addition, the molecular origin of the dipole contribution is explored by using different potential energy functions for water. The results here show that the dipole contribution arises mainly from distortions in the hydration shell of the full hydrogen bonded waters on the liquid side of the interface, which is determined by the charge distribution of the water model. Disturbingly, the potential varies by 0.4 eV depending on the model. Although there is still no consensus on what that charge distribution should be, recent results indicate that it contains both a large quadrupole and negative charge out of the molecular plane, i.e., three-dimensional (3D) charge. Water models with 3D charge give the least distortion of the hydration shell and the best agreement with experimental surface potentials, although there is still uncertainty in the experimental values.

Clathrate hydrates hold considerable promise as safe and economical materials for hydrogen storage. Here we present a quantum mechanical study of H 2 and D 2 diffusion through a hexagonal face shared by two large cages of clathrate hydrates over a wide range of temperatures. Path integral molecular dynamics simulations are used to compute the free-energy profiles for the diffusion of H 2 and D 2 as a function of temperature. Ring polymer molecular dynamics rate theory, incorporating both exact quantum statistics and approximate quantum dynamical effects, is utilized in the calculations of the H 2 and D 2 diffusion rates in a broad temperature interval. We find that the shape of the quantum free-energy profiles and their height relative to the classical free energy barriers at a given temperature, as well as the rate of diffusion, are profoundly affected by competing quantum effects: above 25 K, zero-point energy (ZPE) perpendicular to the reaction path for diffusion between cavities decreases the quantum rate compared to the classical rate, whereas at lower temperatures tunneling outcompetes the ZPE and as result the quantum rate is greater than the classical rate.

Free energy surfaces of chemical and physical systems are often generated using a popular class of enhanced sampling methods that target a set of collective variables (CVs) chosen to distinguish the characteristic features of these surfaces. While some of these approaches are typically limited to low (∼1− 3)-dimensional CV subspaces, methods such as driven adiabatic free-energy dynamics/temperature-accelerated molecular dynamics have been shown to be capable of generating free energy surfaces of quite high dimension by sampling the associated marginal probability distribution via full sweeps over the CV landscape. These approaches repeatedly visit conformational basins, producing a scattering of points within the basins on each visit. Consequently, they are particularly amenable to synergistic combination with regression machine learning methods for filling in the surfaces between the sampled points and for providing a compact and continuous (or semicontinuous) representation of the surfaces that can be easily stored and used for further computation of observable properties. Given the central role of machine learning techniques in this combined approach, it is timely to provide a detailed comparison of the performance of different machine learning strategies and models, including neural networks, kernel ridge regression, support vector machines, and weighted neighbor schemes, for their ability to learn these high-dimensional surfaces as a function of the amount of sampled training data and, once trained, to subsequently generate accurate ensemble averages corresponding to observable properties of the systems. In this article, we perform such a comparison on a set of oligopeptides, in both gas and aqueous phases, corresponding to CV spaces of 2−10 dimensions and assess their ability to provide a global representation of the free energy surfaces and to generate accurate ensemble averages.

The anomalous behavior in the partial molar volumes of ethanol-water mixtures at low concentrations of ethanol is studied using molecular dynamics simulations. Previous work indicates that the striking minimum in the partial molar volume of ethanol VE as a function of ethanol mole fraction XE is determined mainly by water-water interactions. These results were based on simulations that used one water model for the solute-water interactions but two different water models for the water-water interactions. This is confirmed here by using two more water models for the water-water interactions. Furthermore, the previous work indicates that the initial decrease is caused by association of the hydration shells of the hydrocarbon tails, and the minimum occurs at the concentration where all of the hydration shells are touching each other. Thus, the characteristics of the hydration of the tail that cause the decrease and the features of the water models that reproduce this type of hydration are also examined here. The results show that a single-site multipole water model with a charge distribution that mimics the large quadrupole and the p-orbital type electron density out of the molecular plane has "brittle" hydration with hydrogen bonds that break as the tails touch, which reproduces the deep minimum. However, water models with more typical site representations with partial charges lead to flexible hydration that tends to stay intact, which produces a shallow minimum. Thus, brittle hydration may play an essential role in hydrophobic association in water.