Supercritical Water is not Hydrogen Bonded

Quasi-one-dimensional hydrogen bonding in nanoconfined ice

The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics.

Design and Realization of Visual and Contact-Type Fast Charging Power Source

Charging power supplies with both fast and visualization functions have a wide range of applications in the information and new energy industries. In this paper, the visualized and contact-type fast charging power supply based on WO3 film and Zn sheet is presented, and the prototype devices are fabricated. Different with the charging method of conventional batteries, charging is achieved by a Zn sheet contacting with a WO3 film moistened with water, resulting in a rapid discoloration of WO3. Theoretical investigation indicates that the interaction between Zn sheet and water molecules is the primary cause of the color change in the WO3 film. The WO3 film completes the colouring state within 10 s in the presence of Zn sheet and water, and the open-circuit voltage of the device is 0.7 V, which can be used to drive various electronics by series-parallel connection. This research introduces a novel method to induce colouring of WO3 films and proposes a fast charging mode different from traditional power sources. It provides valuable insights for the future development of fast charging in the field of electrical energy.

Solvation Properties of Neutral Gold Species in Supercritical Water Studied By THz Spectroscopy

Supercritical water provides distinctly different solvation properties compared to what is known from liquid water. Despite its prevalence deep in the Earth's crust and its role in chemosynthetic ecosystems in the vicinity of hydrothermal vents, molecular insights into its solvation mechanisms are still very scarce compared to what is known for liquid water. Recently, neutral metal particles have been detected in hydrothermal fluids and proposed to explain the transport of gold species to ore deposits on Earth. Using ab initio molecular dynamics, we elucidate the solvation properties of small gold species at supercritical conditions. The neutral metal clusters themselves contribute enormous THz intensity not because of their intramolecular vibrations, but due to their pronounced electronic polarization coupling to the dynamical supercritical solvent, leading to a continuum absorption up to about 1000 cm-1. On top, long-lived interactions between the gold clusters and solvation water leads at these supercritical conditions to a sharp THz resonance that happens to be close to the one due to H-bonding in liquid water at ambient conditions. The resulting distinct resonances can be used to analyse the solvation properties of neutral metal particles in supercritical aqueous solutions.

Supercritical Water: A Simulation Study to Unravel the Heterogeneity of Its Molecular Structures

(1) Background: In the quest to accurately model the radiolysis of water in its supercritical state, a detailed understanding of water's molecular structure, particularly how water molecules are arranged in this unique state, is essential. (2) Methods: We conducted molecular dynamics simulations using the SPC/E water model to investigate the molecular structures of supercritical water (SCW) over a wide temperature range, extending up to 800 °C. (3) Results: Our results show that at a constant pressure of 25 MPa, the average intermolecular distance around a reference water molecule remains remarkably stable at ~2.9 Å. This uniformity persists across a substantial temperature range, demonstrating the unique heterogeneous nature of SCW under these extreme conditions. Notably, the simulations also reveal intricate patterns within SCW, indicating the simultaneous presence of regions with high and low density. As temperatures increase, we observe a rise in the formation of molecular clusters, which are accompanied by a reduction in their average size. (4) Conclusions: These findings highlight the necessity of incorporating the molecular complexity of SCW into traditional track-structure chemistry models to improve predictions of SCW behavior under ionizing radiation. The study establishes a foundational reference for further exploration of the properties of supercritical water, particularly for its application in advanced nuclear technologies, including the next generation of water-cooled reactors and their small modular reactor variants that utilize SCW as a coolant.

Supercritical fluids behave as complex networks

Supercritical fluids play a key role in environmental, geological, and celestial processes, and are of great importance to many scientific and engineering applications. They exhibit strong variations in thermodynamic response functions, which has been hypothesized to stem from the microstructural behavior. However, a direct connection between thermodynamic conditions and the microstructural behavior, as described by molecular clusters, remains an outstanding issue. By utilizing a first-principles-based criterion and self-similarity analysis, we identify energetically localized molecular clusters whose size distribution and connectivity exhibit self-similarity in the extended supercritical phase space. We show that the structural response of these clusters follows a complex network behavior whose dynamics arises from the energetics of isotropic molecular interactions. Furthermore, we demonstrate that a hidden variable network model can accurately describe the structural and dynamical response of supercritical fluids. These results highlight the need for constitutive models and provide a basis to relate the fluid microstructure to thermodynamic response functions.

Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor

Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is, however, often difficult to understand such spectra at a microscopic level. Ab initio molecular dynamics has repeatedly proved to be suitable for this purpose; however, the computational cost can be daunting. Here, the E(3)-equivariant neural network e3nn is used to fit the atomic polar tensor of liquid water a posteriori on top of existing molecular dynamics simulations. Notably, the introduced methodology is general and thus transferable to any other system as well. The target property is most fundamental and gives access to the IR spectrum, and more importantly, it is a highly powerful tool to directly assign IR spectral features to nuclear motion─a connection which has been pursued in the past but only using severe approximations due to the prohibitive computational cost. The herein introduced methodology overcomes this bottleneck. To benchmark the machine learning model, the IR spectrum of liquid water is calculated, indeed showing excellent agreement with the explicit reference calculation. In conclusion, the presented methodology gives a new route to calculate accurate IR spectra from molecular dynamics simulations and will facilitate the understanding of such spectra on a microscopic level.

Accurate diffusion coefficients of the excess proton and hydroxide in water via extensive ab initio simulations with different schemes

Despite its simple molecular formula, obtaining an accurate in silico description of water is far from straightforward. Many of its very peculiar properties are quite elusive, and in particular, obtaining good estimations of the diffusion coefficients of the solvated proton and hydroxide at a reasonable computational cost has been an unsolved challenge until now. Here, I present extensive results of several unusually long ab initio molecular dynamics (MD) simulations employing different combinations of the Born-Oppenheimer and second-generation Car-Parrinello MD propagation methods with different ensembles (NVE and NVT) and thermostats, which show that these methods together with the RPBE-D3 functional provide a very accurate estimation of the diffusion coefficients of the solvated H₃O⁺ and OH^- ions, together with an extremely accurate description of several properties of neutral water (such as the structure of the liquid and its diffusion and shear viscosity coefficients). In addition, I show that the estimations of D_H3O⁺ and D_OH^- depend dramatically on the simulation length, being necessary to reach timescales in the order of hundreds of picoseconds to obtain reliable results.

Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials

Metal oxide/water interfaces play an important role in biology, catalysis, energy storage and photocatalytic water splitting. The atomistic structure at these interfaces is often difficult to characterize by experimental techniques, whilst results from ab initio molecular dynamics simulations tend to be uncertain due to the limited length and time scales accessible. In this work, we train a committee neural network potential to simulate the hematite/water interface at the hybrid DFT level of theory to reach the nanosecond timescale and systems containing more than 3000 atoms. The NNP enables us to converge dynamical properties, not possible with brute-force ab initio molecular dynamics. Our simulations uncover a rich solvation dynamics at the hematite/water interface spanning three different time scales: picosecond H-bond dynamics between surface hydroxyls and the first water layer, in-plane/out-of-plane tilt motion of surface hydroxyls on the 10 ps time scale, and diffusion of water molecules from the oxide surface characterized by a mean residence lifetime of about 60 ps. Calculation of vibrational spectra confirm that H-bonds between surface hydroxyls and first layer water molecules are stronger than H-bonds in bulk water. Our study showcases how state of the art machine learning approaches can routinely be utilized to explore the structural dynamics at transition metal oxide interfaces with complex electronic structure. It foreshadows that c-NNPs are a promising tool to tackle the sampling problem in ab initio electrochemistry with explicit solvent molecules.

Structures of liquid and aqueous water isotopologues at ambient temperature from ab initio path integral simulations

The heavy hydrogen isotopes D and T are found in trace amounts in water, and when their concentration increases they can play an intricate role in modulating the physical properties of the liquid. We present an analysis of the microscopic structures of ambient light water (H₂O(l)), heavy water (D₂O(l)), T₂O(l), HDO(aq) and HTO(aq) studied by ab initio path integral molecular dynamics (PIMD). Unlike previous ab initio PIMD investigations of H₂O(l) and D₂O(l) [Chen et al., Phys. Rev. Lett., 2003, 91, 215503] [Machida et al., J. Chem. Phys., 2017, 148, 102324] we find that D₂O(l) is more structured than H₂O(l), as is predicted by the experiment. The agreement between the experiment and our simulation for H₂O(l) and D₂O(l) allows us to accurately predict the intra- and intermolecular structures of T₂O(l) HDO(aq) and HTO(aq). T₂O(l) is found to have a similar intermolecular structure to that of D₂O(l), while the intramolecular structure is more compact, giving rise to a smaller dipole moment than those of H₂O(l) and D₂O(l). For the mixed isotope species, HDO(aq) and HTO(aq), we find smaller dipole moments and fewer hydrogen bonds when compared with the pure species H₂O and D₂O. We can attribute this effect to the relative compactness of the mixed isotope species, which results in a lower dipole moment than that of the pure species.

Ab Initio Simulations of Water/Metal Interfaces

Structures and processes at water/metal interfaces play an important technological role in electrochemical energy conversion and storage, photoconversion, sensors, and corrosion, just to name a few. However, they are also of fundamental significance as a model system for the study of solid-liquid interfaces, which requires combining concepts from the chemistry and physics of crystalline materials and liquids. Particularly interesting is the fact that the water-water and water-metal interactions are of similar strength so that the structures at water/metal interfaces result from a competition between these comparable interactions. Because water is a polar molecule and water and metal surfaces are both polarizable, explicit consideration of the electronic degrees of freedom at water/metal interfaces is mandatory. In principle, ab initio molecular dynamics simulations are thus the method of choice to model water/metal interfaces, but they are computationally still rather demanding. Here, ab initio simulations of water/metal interfaces will be reviewed, starting from static systems such as the adsorption of single water molecules, water clusters, and icelike layers, followed by the properties of liquid water layers at metal surfaces. Technical issues such as the appropriate first-principles description of the water-water and water-metal interactions will be discussed, and electrochemical aspects will be addressed. Finally, more approximate but numerically less demanding approaches to treat water at metal surfaces from first-principles will be briefly discussed.

Ab initio study of nuclear quantum effects on sub- and supercritical water

The structures of water in the ambient, subcritical, and supercritical conditions at various densities were studied systematically by ab initio path integral molecular dynamics simulations. It was found that the nuclear quantum effects (NQEs) have a significant impact on the structure of hydrogen bonds in close contact, not only in the ambient condition but also in the sub- and supercritical conditions. The NQEs on the structure beyond the hydrogen bond contact are important in ambient water, but not much for water in the sub- and supercritical conditions. The NQEs are furthermore important for determining the number of hydrogen bonds in the ambient conditions, and this role is, however, diminished in the sub- and supercritical conditions. The NQEs do, nevertheless, show their importance in determining the intramolecular structure of water and the close contact structures of the hydrogen bonds, even at sub- and supercritical conditions. Using the RPBE-D3 functional, the computed radial distribution functions for ambient water are in excellent agreement with experimental data, upgrading our previous results using the BLYP-D2 functional [Machida et al., J. Chem. Phys. 148, 102324 (2018)]. The computed radial distribution functions for water in the sub- and supercritical conditions were carefully compared with experiment. In particular, we found that the first peak in hydrogen pair distribution functions matches only when the NQEs are taken into account.

Machine learning potentials for complex aqueous systems made simple

Understanding complex materials, in particular those with solid–liquid interfaces, such as water on surfaces or under confinement, is a key challenge for technological and scientific progress. Although established simulation approaches have been able to provide important atomistic insight, ab initio techniques struggle with the required time and length scales, while force field methods can often be limited in terms of their accuracy. Here we show how these limitations can be overcome in a simple and automated machine learning procedure to provide accurate models of interactions at the ab initio level, as illustrated for a variety of complex aqueous systems. These developments open up the prospect of the straightforward exploration of many technologically relevant systems by molecular simulations.

Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid–liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems.

Hydrogen Bonding and the Structural Properties of Glycerol-Water Mixtures with a Microwave Field: a Molecular Dynamics Study

In a microwave field, the dielectric properties, molecular structures, and hydrogen bonding dynamics of glycerol in its mixtures with water were determined by the molecular dynamics simulation method. The dipole-dipole correlation of glycerol is linked to the field intensity of microwaves. The results show that as the field intensity is increased, even glycerol in the second coordination shell can become correlated with each other. The structures of up to 35 glycerol molecules are observed. More than that, it was observed that lifetimes of glycerol-glycerol hydrogen bonds were prolonged, while the average hydrogen bond number was also increased. Besides, the structures in a strong microwave field mimic the weak C-H⋯O hydrogen bonds seen in high-glycerol concentration mixtures, yet the concentration is lower. These results indicate that with the assistance of the microwave field, glycerol molecules become concentrated and are more likely to establish stable interactions with others. As a consequence, the spherical clusters composed by glycerol molecules in our nanosheet synthesis experiment are easier to form.

Ion association in hydrothermal aqueous NaCl solutions: implications for the microscopic structure of supercritical water

Knowledge of the microscopic structure of fluids and changes thereof with pressure and temperature is important for the understanding of chemistry and geochemical processes. In this work we investigate the influence of sodium chloride on the hydrogen-bond network in aqueous solution up to supercritical conditions. A combination of in situ X-ray Raman scattering and ab initio molecular dynamics simulations is used to probe the oxygen K-edge of the alkali halide aqueous solution in order to obtain unique information about the oxygen's local coordination around the ions, e.g. solvation-shell structure and the influence of ion pairing. The measured spectra exhibit systematic temperature dependent changes, which are entirely reproduced by calculations on the basis of structural snapshots obtained via ab initio molecular dynamics simulations. Analysis of the simulated trajectories allowed us to extract detailed structural information. This combined analysis reveals a net destabilizing effect of the dissolved ions which is reduced with rising temperature. The observed increased formation of contact ion pairs and occurrence of larger polyatomic clusters at higher temperatures can be identified as a driving force behind the increasing structural similarity between the salt solution and pure water at elevated temperatures and pressures with drawback on the role of hydrogen bonding in the hot fluid. We discuss our findings in view of recent results on hot NaOH and HCl aqueous fluids and emphasize the importance of ion pairing in the interpretation of the microscopic structure of water.

First-Principles Simulations of CuCl in High-Temperature Water Vapor

Experimental data suggest that the solubility of copper in high-temperature water vapor is controlled by the formation of hydrated clusters of the form CuCl(H₂O)_n, where the average number of water molecules in the cluster generally increases with increasing density [Migdisov, A. A.; et al. Geochim. Cosmochim. Acta 2014, 129, 33-53]. However, the precise nature of these clusters is difficult to probe experimentally. Moreover, there are some discrepancies between experimental estimates of average cluster size and prior simulation work [Mei, Y. Geofluids 2018, 2018, 4279124]. We have performed first-principles Monte Carlo (MC) and molecular dynamics (MD) simulations to explore these clusters in finer detail. We find that molecular dynamics is not the most appropriate technique for studying aggregation in vapor phases, even at relatively high temperatures. Specifically, our MD simulations exhibit substantial problems in adequately sampling the equilibrium cluster size distribution. In contrast, MC simulations with specialized cluster moves are able to accurately sample the phase space of hydrogen-bonding vapors. At all densities, we find a stable, slightly distorted linear H₂O-Cu-Cl structure, which is in agreement with the earlier simulations, surrounded by a variable number of water molecules. The surrounding water molecules do not form a well-defined second solvation shell but rather a loose network of hydrogen-bonded water with molecular CuCl on the outside edge of the water cluster. We also find a broad distribution of hydration numbers, especially at higher densities. In contrast to previous simulation work but in agreement with experimental data, we find that the average hydration number substantially increases with increasing density. Moreover, the value of the hydration number depends on the choice of cluster definition.