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Register a new userQuantum fluctuations and isotope effects in ab initio descriptions of water
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Nuclear quantum effects, such as zero-point energy and tunneling, cause significant changes to the structure and dynamics of hydrogen bonded systems such as liquid water. However, due to the current inability to simulate liquid water using an exact description of its electronic structure, the interplay between nuclear and electronic quantum effects remains unclear. Here we use simulations that incorporate the quantum mechanical nature of both the nuclei and electrons to provide a fully ab initio determination of the particle quantum kinetic energies, free energy change upon exchanging hydrogen for deuterium and the isotope fractionation ratio in water. These properties, which selectively probe the quantum nature of the nuclear degrees of freedom, allow us to make direct comparison to recent experiments and elucidate how electronic exchange and correlation and nuclear quantum fluctuations determine the structure of the hydrogen bond in water.
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An understanding of density fluctuations in bulk water has made significant contributions to our understanding of the hydration and interactions of idealized, purely repulsive hydrophobic solutes. To similarly inform the hydration of realistic hydrophobic solutes that have dispersive interactions with water, here we characterize water density fluctuations in the presence of attractive fields that correspond to solute-water attractions. We find that when the attractive field acts only in the solute hydration shell, but not in the solute core, it does not significantly alter water density fluctuations in the solute core region. We further find that for a wide range of solute sizes and attraction strengths, the free energetics of turning on the attractive fields in bulk water are accurately captured by linear response theory. Our results also suggest strategies for more efficiently estimating hydration free energies of realistic solutes in bulk water and at interfaces.
Second-Harmonic Scatteringh (SHS) experiments provide a unique approach to probe non-centrosymmetric environments in aqueous media, from bulk solutions to interfaces, living cells and tissue. A central assumption made in analyzing SHS experiments is that the each molecule scatters light according to a constant molecular hyperpolarizability tensor $boldsymbol{beta}^{(2)}$. Here, we investigate the dependence of the molecular hyperpolarizability of water on its environment and internal geometric distortions, in order to test the hypothesis of constant $boldsymbol{beta}^{(2)}$. We use quantum chemistry calculations of the hyperpolarizability of a molecule embedded in point-charge environments obtained from simulations of bulk water. We demonstrate that both the heterogeneity of the solvent configurations and the quantum mechanical fluctuations of the molecular geometry introduce large variations in the non-linear optical response of water. This finding has the potential to change the way SHS experiments are interpreted: in particular, isotopic differences between H$_2$O and D$_2$O could explain recent second-harmonic scattering observations. Finally, we show that a simple machine-learning framework can predict accurately the fluctuations of the molecular hyperpolarizability. This model accounts for the microscopic inhomogeneity of the solvent and represents a first step towards quantitative modelling of SHS experiments.
Adding salt to water at ambient pressure affects its thermodynamic properties. At low salt concentration, anomalies such as the density maximum are shifted to lower temperature, while at large enough salt concentration they cannot be observed any more. Here we investigate the effect of salt on an anomaly recently observed in pure water at negative pressure: the existence of a sound velocity minimum along isochores. We compare experiments and simulations for an aqueous solution of sodium chloride with molality around $1.2,mathrm{mol,kg^{-1}}$, reaching pressures beyond $-100,mathrm{MPa}$. We also discuss the origin of the minima in the sound velocity and emphasize the importance of the relative position of the temperatures of sound velocity and density anomalies.
A comprehensive microscopic understanding of ambient liquid water is a major challenge for $ab$ $initio$ simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g., H or D), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g., isotope effects), and therefore provide yet another challenge for $ab$ $initio$ approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretized path-integral (PI) approach, and machine learning (ML) constitutes a versatile $ab$ $initio$ based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model---a neural-network representation of the $ab$ $initio$ PES---in conjunction with a PI approach based on the generalized Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H$_2$O and D$_2$O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects.
Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice I{it h} at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.
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