Mass Density Fluctuations in Quantum and Classical descriptions of Liquid Water

First principles molecular dynamics simulation protocol is established using revised functional of Perdew-Burke-Ernzerhof (revPBE) in conjunction with Grimme's third generation of dispersion (D3) correction to describe properties of water at ambient conditions. This study also demonstrates the consistency of the structure of water across both isobaric (NpT) and isothermal (NVT) ensembles. Going beyond the standard structural benchmarks for liquid water, we compute properties that are connected to both local structure and mass density uctuations that are related to concepts of solvation and hydrophobicity. We directly compare our revPBE results to the Becke-Lee-Yang-Parr (BLYP) plus Grimme dispersion corrections (D2) and both the empirical fixed charged model (SPC/E) and many body interaction potential model (MB-pol) to further our understanding of how the computed properties herein depend on the form of the interaction potential

Quantifying the hydration structure of sodium and potassium ions: taking additional steps on Jacob's Ladder

The ability to reproduce the experimental structure of water around the sodium and potassium ions is a key test of the quality of interaction potentials due to the central importance of these ions in a wide range of important phenomena. Here, we simulate the Na+ and K+ ions in bulk water using three density functional theory functionals: (1) the generalized gradient approximation (GGA) based dispersion corrected revised Perdew, Burke, and Ernzerhof functional (revPBE-D3) (2) the recently developed strongly constrained and appropriately normed (SCAN) functional (3) the random phase approximation (RPA) functional for potassium. We compare with experimental X-ray diffraction (XRD) and X-ray absorption fine structure (EXAFS) measurements to demonstrate that SCAN accurately reproduces key structural details of the hydration structure around the sodium and potassium cations, whereas revPBE-D3 fails to do so. However, we show that SCAN provides a worse description of pure water in comparison with revPBE-D3. RPA also shows an improvement for K+, but slow convergence prevents rigorous comparison. Finally, we analyse cluster energetics to show SCAN and RPA have smaller fluctuations of the mean error of ion-water cluster binding energies compared with revPBE-D3

The Role of Hydrogen Bonding in the Decomposition of H₂CO₃ in Water: Mechanistic Insights from Ab Initio Metadynamics Studies of Aqueous Clusters

Both concerted and stepwise mechanisms have been proposed for the decomposition of H₂CO₃ in bulk water based on electronic structure and ab initio molecular dynamics calculations. To consistently determine which, if any, mechanism predominates in bulk water, we performed ab initio metadynamics simulations of the decomposition of H₂CO₃ in water clusters of increasing size. We found that, in the small clusters (containing six and nine water molecules), the decomposition occurs according to a concerted proton shuttle mechanism via a cyclic transition state, whereas, in the larger clusters (containing 20 and 45 water molecules), the decomposition occurs according to a two-step mechanism via a solvent-separated HCO₃^–/H₃O⁺ ion pair intermediate. Due to the additional water molecules in the larger clusters, the dissociation of H₂CO₃ into the metastable solvent-separated ion pair was found to be energetically favorable, thereby preventing the formation of the cyclic transition state and committing the decomposition to the sequential route. An analysis of the solvation environment around the H₂CO₃ molecule in the various clusters revealed that the transition from the concerted mechanism to the stepwise mechanism precisely hinges upon the number of water molecules hydrogen bonded to the H₃O⁺ intermediate, which changes as the size of the cluster increases. The larger clusters contain a sufficient number of water molecules to fully solvate the H₃O⁺ intermediate, indicating that they can provide a bulk-like environment for this reaction. Therefore, these results strongly demonstrate that the decomposition of H₂CO₃ in bulk water occurs via the stepwise mechanism

Critical review on gas hydrate formation at solid surfaces and in confined spaces—why and how does interfacial regime matter?

Gas hydrates are crystalline solids composed of water and gases. They occur abundantly in nature and are potentially significant to industry. Solid surfaces and confined spaces strongly affect the formation of gas hydrates. Research into this particular topic is active, particularly aiming to understand the effects of solid surfaces and confinements on gas hydrate formation and using functional solids for controlling the formation kinetics. Experimental observations appear to vary from one solid to another. The observations demand a knowledge of (1) why the effects vary among the solids and (2) what factors are determining. Here, we critically review experimental observations, discuss the underlying mechanisms, and generalize the literature findings for a better understanding of the mechanism. It is inferred that open hydrophobic solids can promote gas hydrate formation via a tetrahedral ordering of water and an increased density of gases at the solid−water interfaces. Open hydrophilic solids hinder gas hydrate formation via a distorted water structure and a depleted density of gases at the solid−water interfaces. Confining solids have rather complex effects due to the complexity of wetting in confined spaces. Therefore, confined media with moderate wettability and partial water saturation might provide optimum conditions for gas hydrate formation

Learning intermolecular forces at liquid–vapor interfaces

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. We find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated