We introduce general Monte Carlo simulation methods for determining the wetting and drying properties of model systems. We employ an interface-potential-based approach in which the interfacial properties of a system are related to the surface excess free energy of a thin fluid film in contact with a surface. Two versions of this approach are explored: a "spreading" method focused on the growth of a thin liquid film from a surface in a mother vapor and a "drying" method focused on the growth of a thin vapor film from a surface in a mother liquid. The former provides a direct measure of the spreading coefficient while the latter provides an analogous drying coefficient. When coupled with an independent measure of the liquid-vapor surface tension, these coefficients enable one to compute the contact angle. We also show how one can combine information gathered from application of the spreading and drying methods at a common state point to obtain direct measures of the contact angle and liquid-vapor surface tension. The computational strategies introduced here are applied to two model systems. One includes a monatomic Lennard-Jones fluid that interacts with a structureless substrate via a long-ranged substrate potential. The second model contains a monatomic Lennard-Jones fluid that interacts with an atomistically detailed substrate via a short-ranged potential. Expanded ensemble techniques are coupled with the interface potential approach to compile the temperature- and substrate strength-dependence of various interfacial properties for these systems. Overall, we find that the approach pursued here provides an efficient and precise means to calculate the wetting and drying properties of model systems.

We use molecular simulation to study the wetting behavior of water near flat nonpolar surfaces. The interface potential approach is used to capture the evolution of various interfacial properties over a broad range of temperature and substrate strength. Three model substrates are considered: an atomistically detailed face-centered cubic (FCC) lattice, a graphite lattice, and a structureless wall. We first examine the evolution of the contact angle with substrate strength for conditions ranging from near drying to complete wetting at select temperatures. A notable characteristic of all systems is the presence of a surface strength at which the contact angle is independent of temperature. We also identify an analogous point in which the quantity γ lv cos θ is temperature invariant. We discuss the relationship between this point and the excess entropy of the solid−liquid interface. We next consider the temperature dependence of interfacial properties. We find the contact angle to decrease with temperature at relatively strong surfaces and increase with the temperature at relatively weak surfaces. The work of adhesion is found to be a useful quantity for describing the interfacial properties of water. The enthalpic and the entropic contributions to the work of adhesion are obtained from its temperature dependence. These properties are found to be directly related to the affinity of water for a solid surface and therefore serve as useful measures of the hydrophobicity of a surface. Finally, we examine the effect of surface strength and temperature on the density depletion associated with water at hydrophobic surfaces. We study various metrics that quantify the density and compressibility of water in the vicinity of hydrophobic surfaces. Comparisons are drawn between the behavior of water and simple nonpolar fluids at solvophobic surfaces.

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-thecluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum, however the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local-search based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global-search based techniques, such as simulated annealing, tabu search, and genetic algorithms may offer better quality results but can be too time consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization (QUBO) problem and discuss two clustering algorithms which are then implemented on commercially-available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

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We use molecular simulations with a united atom force field to examine the effect of short chain branching (SCB) on the noncrystalline, interlamellar structure typical of linear low density polyethylene (LLDPE). The model is predicated on a metastable thermodynamic equilibrium within the interlamellar space of the crystal stack and accounts explicitly for the various chain topologies (loops, tails, and bridges) therein. We examine three branched systems containing methyl, ethyl, and butyl side branches, and compare our results to high density polyethylene (HDPE), without branches. We also compare results for two united atom force fields, PYS and TraPPE-UA, within the context of these simulations. In contrast to conventional wisdom, our simulations indicate that the thicknesses of the interfacial regions in systems with SCB are smaller than those observed for a linear polyethylene without branches, and that branches are uniformly distributed throughout the interlamellar region. We find a prevalence of gauche states along the backbone due to the presence of branches, and an abrupt decrease in the orientational order in the region immediately adjacent to the crystallite.3 Introduction: