Relationships between Molecular Structural Order Parameters and Equilibrium Water Dynamics in Aqueous Mixtures

Abstract

Water’s unique thermophysical properties and how it mediates aqueous interactions between solutes have long been interpreted in terms of its collective molecular structure. The seminal work of Errington and Debenedetti [Nature 2001, 409, 318–321] revealed a striking hierarchy of relationships among the thermodynamic, dynamic, and structural properties of water, motivating many efforts to understand (1) what measures of water structure are connected to different experimentally accessible macroscopic responses and (2) how many such structural metrics are adequate to describe the collective structural behavior of water. Diffusivity constitutes a particularly interesting experimentally accessible equilibrium property to investigate such relationships because advanced NMR techniques allow the measurement of bulk and local water dynamics in nanometer proximity to molecules and interfaces, suggesting the enticing possibility of measuring local diffusivities that report on water structure. Here, we apply statistical learning methods to discover persistent structure–dynamic correlations across a variety of simulated aqueous mixtures, from alcohol–water to polypeptoid–water systems. We investigate a variety of molecular water structure metrics and find that an unsupervised statistical learning algorithm (namely, sequential feature selection) identifies only two or three independent structural metrics that are sufficient to predict water self-diffusivity accurately. Surprisingly, the translational diffusivity of water across all mixed systems studied here is strongly correlated with a measure of tetrahedral order given by water’s triplet angle distribution. We also identify a separate small number of structural metrics that well predict an important thermodynamic property, the excess chemical potential of an idealized methane-sized hydrophobe in water. Ultimately, we offer a Bayesian method of inferring water structure by using only structure–dynamics linear regression models with experimental Overhauser dynamic nuclear polarization (ODNP) measurements of water self-diffusivity. This study thus quantifies the relationships among several distinct structural order parameters in water and, through statistical learning, reveals the potential to leverage molecular structure to predict fundamental thermophysical properties. In turn, these findings suggest a framework for solving the inverse problem of inferring water’s molecular structure using experimental measurements such as ODNP studies that probe local water properties.

Bridging the Gap in Cryopreservation Mechanism: Unraveling the Interplay between Structure, Dynamics, and Thermodynamics in Cryoprotectant Aqueous Solutions

“…Structure-dynamics relationships in aqueous environments have previously been explored in the context of supercooled water, − hydration water, ,,,,− and aqueous mixtures. ,,− In a recent computational study, we detailed emergent structure-diffusivity relations across a broad array of binary mixtures of varying temperature, composition, and cosolvent chemical identity . Furthermore, we demonstrated that simple linear regression yields quantitative predictions of water self-diffusivities for 59 distinct simulations using only p tet and two additional water structural metrics.…”

“…To complement the structural motifs from the experimental and computational analyses discussed above, we introduce an experimental approach to obtain the diffusion of water in DMSO mixtures. Prior studies suggest that aqueous systems exhibit strong relationships between nanoscale water structure and dynamics. − To directly probe water dynamics, we measure water diffusion coefficients, D ODNP , in water–DMSO using Overhauser dynamic nuclear polarization (ODNP) experiments. In Figure c, we depict the log-normalized ODNP water diffusion coefficient, ln false[ D ODNP / D ODNP , pure false] , against the normalized, MD-computed tetrahedral population, p tet / p tet , pure , for X DMSO = 0.01, 0.06, 0.10, 0.20, and 0.30.…”

“…47,61,72−74 In a recent computational study, we detailed emergent structure-diffusivity relations across a broad array of binary mixtures of temperature, composition, and cosolvent chemical identity. 61 Furthermore, we demonstrated that simple linear regression yields quantitative predictions of water self-diffusivities for 59 distinct simulations using only p tet and two additional water structural metrics. The structuredynamics connection shown in Figure 3c expands on these prior works by demonstrating the persistence of structure and dynamics even when they are derived computationally and experimentally, respectively.…”

Relationships between Water’s Structure and Solute Affinity at Polypeptoid Brush Surfaces

“…To provide an alternate perspective, we characterize how the surfaces perturb water’s structural orientational correlations. Specifically, we investigate the water triplet or three-body angle distribution P (θ) that has previously been shown to correlate with measures of water dynamics , and hydration free energy. , This distribution characterizes the populations of angles θ between all triplets of neighboring water molecules (angles measured between oxygen centers) and can show shifts in orientational structure in several key regimes: a tetrahedral population near θ = 109.5°, an icosahedral population typical of simple liquids around θ = 63.4°, and an ideal gas-like population centered near θ = 90°. , The tetrahedral and icosahedral populations are hypothesized to reflect changes in small-length scale hydrophobic responses as water around small solutes has been shown to possess enhanced tetrahedrality and reduced icosahedrality . On the other hand, the ideal gas population reflects changes in large-length scale hydrophobic responses, as water around a large hydrophobic surface has been shown to be closer to its vapor–liquid equilibrium and therefore exhibits larger density fluctuations, spontaneously forming larger voids. , These small and large length scale behaviors correspond to qualitative changes in the thermodynamics of solvation .…”

“…To quantify and simplify these shifts in the three-body angle distributions, we compute their projections onto the principal directions along which they change. Specifically, our prior work examined shifts in triplet distributions for a wide range of aqueous mixtures across different temperatures, compositions, and chemistries, and using principal component analysis, found that the distributions only varied along three principal components, as shown in Figure d–f. This work hypothesized that the first principal component, with peaks in the tetrahedral and icosahedral regions, captures changes in the water structure around small-length scale hydrophobic solutes; for instance, the three-body angle distribution around methane looks very similar to this component.…”