Dissolving salt is not equivalent to applying a pressure on water

Do Specific Ion Effects on Collective Relaxation Arise from Perturbation of Hydrogen-Bonding Network Structure?

The change in the transport properties (i.e., water diffusivity, shear viscosity, etc.) when adding salts to water has been used to classify ions as either being chaotropic or kosmotropic, a terminology based on the presumption that this phenomenon arises from respective breakdown or enhancement of the hydrogen-bonding network structure. Recent quasi-elastic neutron scattering measurements of the collective structural relaxation time, τC, in aqueous salt solutions were interpreted as confirming this proposed origin of ion effects on the dynamics of water. However, we find similar changes in τC in the same salt solutions based on molecular dynamics (MD) simulations using a coarse-grained water model in which no hydrogen bonding exists, challenging this conventional interpretation of mobility change resulting from the addition of salts to water. A thorough understanding of specific ion effects should be useful in diverse material manufacturing and biomedical applications, where these effects are prevalent, but poorly understood.

Response of Ionic Hydration Structure and Selective Transport Behavior to Aqueous Solution Chemistry during Nanofiltration

The effect of aqueous solution chemistry on the ionic hydration structure and its corresponding nanofiltration (NF) selectivity is a research gap concerning ion-selective transport. In this study, the hydration distribution of two typical monovalent anions (Cl- and NO3-) under different aqueous solution chemical conditions and the corresponding transmembrane selectivity during NF were investigated by using in situ liquid time-of-flight secondary ion mass spectrometry in combination with molecular dynamics simulations. We demonstrate the inextricable link between the ion hydration structure and the pore steric effect and further find that ionic transmembrane transport can be regulated by breaking the balance between the hydrogen bond network (i.e., water-water) and ion hydration (i.e., ion-water) interactions of hydrated ion. For strongly hydrated (H2O)nCl- with more intense ion-water interactions, a higher salt concentration and coexisting ion competition led to a larger hydrated size and, thus, a higher ion rejection by the NF membrane, whereas weakly hydrated (H2O)nNO3- takes the reverse under the same conditions. Stronger OH--anion hydration competition resulted in a smaller hydrated size of (H2O)nCl- and (H2O)nNO3-, showing a lower observed average hydration number at pH 10.5. This study deepens the long-overlooked understanding of NF separation mechanisms, concerning the hydration structure.

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks

Zeolitic imidazolate frameworks (ZIFs) feature complex phase transitions, including polymorphism, melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of ab initio molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4 (Zn(Im)2, Im = C3H3N2-) glasses, as well as for the formation of ZIF-zni (Zn(Im)2) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks (MOFs) and may thus guide the development of MOF glasses.

Determining the mechanical and decomposition properties of high energetic materials (α-RDX, β-HMX, and ε-CL-20) using a neural network potential

Molecular simulations of high energetic materials (HEMs) are limited by efficiency and accuracy. Recently, neural network potential (NNP) models have achieved molecular simulations of millions of atoms while maintaining the accuracy of density functional theory (DFT) levels. Herein, an NNP model covering typical HEMs containing C, H, N, and O elements is developed. The mechanical and decomposition properties of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by employing the molecular dynamics (MD) simulations based on the NNP model. The calculated results show that the mechanical properties of α-RDX, β-HMX, and ε-CL-20 agree with previous experiments and theoretical results, including cell parameters, equations of state, and elastic constants. In the thermal decomposition simulations, it is also found that the initial decomposition reactions of the three crystals are N-NO2 homolysis, corresponding radical intermediates formation, and NO2-induced reactions. This decomposition trajectory is mainly divided into two stages separating from the peak of NO2: pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements in this work is an alternative reactive force field for RDX, HMX, and CL-20 HEMs, and it opens up new potential for future kinetic study of nitramine explosives.

Deciphering Density Fluctuations in the Hydration Water of Brownian Nanoparticles via Upconversion Thermometry

We investigate the intricate relationship among temperature, pH, and Brownian velocity in a range of differently sized upconversion nanoparticles (UCNPs) dispersed in water. These UCNPs, acting as nanorulers, offer insights into assessing the relative proportion of high-density and low-density liquid in the surrounding hydration water. The study reveals a size-dependent reduction in the onset temperature of liquid-water fluctuations, indicating an augmented presence of high-density liquid domains at the nanoparticle surfaces. The observed upper-temperature threshold is consistent with a hypothetical phase diagram of water, validating the two-state model. Moreover, an increase in pH disrupts the organization of water molecules, similar to external pressure effects, allowing simulation of the effects of temperature and pressure on hydrogen bonding networks. The findings underscore the significance of the surface of suspended nanoparticles for understanding high- to low-density liquid fluctuations and water behavior at charged interfaces.

Molecular Insights into the Influence of Ions on the Water Structure. I. Alkali Metal Ions in Solution

In this study, we explore the impact of alkali metal ions (Li+, Na+, K+, Rb+, and Cs+) on the hydration structure of water using molecular dynamics simulations carried out with MB-nrg potential energy functions (PEFs). Our analyses include radial distribution functions, coordination numbers, dipole moments, and infrared spectra of water molecules, calculated as a function of solvation shells. The results collectively indicate a highly local influence of all of the alkali metal ions on the hydrogen-bond network established by the surrounding water molecules, with the smallest and most densely charged Li+ ion exerting the most pronounced effect. Remarkably, the MB-nrg PEFs demonstrate excellent agreement with available experimental data for the position and size of the first solvation shells, underscoring their potential as predictive models for realistic simulations of ionic aqueous solutions across various thermodynamic conditions and environments.

Water-in-Salt: Fast Dynamics, Structure, Thermodynamics, and Bulk Properties

We present concentration-dependent dynamics of highly concentrated LiBr solutions and LiCl temperature-dependent dynamics for two high concentrations and compare the results to those of prior LiCl concentration-dependent data. The dynamical data are obtained using ultrafast optical heterodyne-detected optical Kerr effect (OHD-OKE). The OHD-OKE decays are composed of two pairs of biexponentials, i.e., tetra-exponentials. The fastest decay (t1) is the same as pure water's at all concentrations within error, while the second component (t2) slows slightly with concentration. The slower components (t3 and t4), not present in pure water, slow substantially, and their contributions to the decays increase significantly with increasing concentration, similar to LiCl solutions. Simulations of LiCl solutions from the literature show that the slow components arise from large ion/water clusters, while the fast components are from ion/water structures that are not part of large clusters. Temperature-dependent studies (15-95 °C) of two high LiCl concentrations show that decreasing the temperature is equivalent to increasing the room temperature concentration. The LiBr and LiCl concentration dependences and the two LiCl concentrations' temperature dependences all have bulk viscosities that are linearly dependent on τcslow, the correlation time of the slow dynamics (weighted averages of t3 and t4). Remarkably, all four viscosity vs 1/τCslow plots fall on the same line. Application of transition state theory to the temperature-dependent data yields the activation enthalpies and entropies for the dynamics of the large ion/water clusters, which underpin the bulk viscosity.

Combined Effects of Pressure and Ionic Strength on Protein-Protein Interactions: An Empirical Approach

Proteins are exposed to hydrostatic pressure (HP) in a variety of ecosystems as well as in processing steps such as freeze-thaw, cell disruption, sterilization, and homogenization, yet pressure effects on protein-protein interactions (PPIs) remain underexplored. With the goal of contributing toward the expanded use of HP as a fundamental control parameter in protein research, processing, and engineering, small-angle X-ray scattering was used to examine the effects of HP and ionic strength on ovalbumin, a model protein. Based on an extensive data set, we develop an empirical method for scaling PPIs to a master curve by combining HP and osmotic effects. We define an effective pressure parameter that has been shown to successfully apply to other model protein data available in the literature, with deviations evident for proteins that do not follow the apparent Hofmeister series. The limitations of the empirical scaling are discussed in the context of the hypothesized underlying mechanisms.

Free-energy decomposition of salt effects on the solubilities of small molecules and the role of excluded-volume effects

The roles of cations and anions are different in the perturbation on solvation, and thus, the analyses of the separated contributions from cations and anions are useful to establish molecular pictures of ion-specific effects. In this work, we investigate the effects of cations, anions, and water separately in the solvation of n-alcohols and n-alkanes by free-energy decomposition. By utilising energy-representation theory of solvation, we address the contributions arising from the direct solute-solvent interactions and the excluded-volume effects. It is found that the change in solvation of n-alcohols and n-alkanes upon addition of salt depends primarily on the anion species. The direct interaction between the anion and solute is in agreement with the Setschenow coefficient in terms of the ranking of salting-in and salting-out for n-alkanes, which corresponds to the extent of accumulation of the anion on the solute surface. For each of the n-alcohols and n-alkanes examined, the excluded-volume component in the Setschenow coefficient is well correlated to the (total) Setschenow coefficient when the salt effects are concerned. The ranking of the excluded-volume component in the variation of the salt species is parallel to the water contribution, which is correlated further to the change in the water density upon the addition of the salt.

On the Elusive Links between Solution Microstructure, Dynamics, and Solvation Thermodynamics: Demystifying the Path through a Bridge over Troubled Conjectures and Misinterpretations

We build a fundamentally based bridge between the solute-induced microstructural perturbation of the species environment and the dynamic as well as thermodynamic responses of the fluid system, regardless of the state conditions, composition, nature of the solvent, and either the magnitude or the type of solute-solvent intermolecular-interaction asymmetries. For that purpose, we advance a fluctuation-based solvation formalism of fluid mixtures to provide meaningful descriptors of solvation phenomena, the microstructural signatures of their solute-solvent intermolecular interaction asymmetry, and the thermodynamic manifestations linked to the solution nonideality. The rigorous foundations afford us to address some crucial issues frequently invoked in the literature including the microstructural perturbation domain, its proper identification and molecular-based meaning toward the interpretation of the solvation process, and the potential impact of the local differential behavior between anions and cations on the actual salt-induced perturbation of the solvent microstructure. Indeed, we link the precisely characterized species solvation behavior to fundamental thermodynamic residual-property relations, and the dynamics associated with either the viscous flow or diffusive behavior of the solvent, to finally illustrate their outcome with experimental data of aqueous electrolyte solutions from the available literature. Ultimately, this effort provides a highly desirable unambiguous identification of the cause-effect connections between the microstructurally perturbed domains and the experimentally measured macroscopic solvation properties, including their effect on the dynamics of the solvent environment. More importantly, it lends a well-established solvation framework to bridge rigorously the microstructural details of the mixture, its dynamics, and its solvation thermodynamics to enhance our understanding of well-defined ranked Hofmeister series, i.e., by avoiding ad hoc conjectures and unsupported microscopic interpretations of solvation phenomena.

Exploring the impact of ions on oxygen K-edge X-ray absorption spectroscopy in NaCl solution using the GW-Bethe-Salpeter-equation approach

X-ray absorption spectroscopy (XAS) is a powerful experimental tool to probe the local structure in materials with the core hole excitations. Here, the oxygen K-edge XAS spectra of the NaCl solution and pure water are computed by using a recently developed GW-Bethe-Salpeter equation approach, based on configurations modeled by path-integral molecular dynamics with the deep-learning technique. The neural network is trained on ab initio data obtained with strongly constrained and appropriately normed density functional theory. The observed changes in the XAS features of the NaCl solution, compared to those of pure water, are in good agreement between experimental and theoretical results. We provided detailed explanations for these spectral changes that occur when NaCl is solvated in pure water. Specifically, the presence of solvating ion pairs leads to localization of electron-hole excitons. Our theoretical XAS results support the theory that the effects of the solvating ions on the H-bond network are mainly confined within the first hydration shell of ions, however beyond the shell the arrangement of water molecules remains to be comparable to that observed in pure water.

High-Throughput Aqueous Electrolyte Structure Prediction Using IonSolvR and Equivariant Graph Neural Network Potentials

Neural network potentials have recently emerged as an efficient and accurate tool for accelerating ab initio molecular dynamics (AIMD) in order to simulate complex condensed phases such as electrolyte solutions. Their principal limitation, however, is their requirement for sufficiently large and accurate training sets, which are often composed of Kohn-Sham density functional theory (DFT) calculations. Here we examine the feasibility of using existing density functional tight-binding (DFTB) molecular dynamics trajectory data available in the IonSolvR database in order to accelerate the training of E(3)-equivariant graph neural network potentials. We show that the solvation structure of Na+ and Cl- in aqueous NaCl solutions can be accurately reproduced with remarkably small amounts of data (i.e., 100 MD frames). We further show that these predictions can be systematically improved further via an embarrassingly parallel resampling approach.

Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials

The diffusivity of water in aqueous cesium iodide solutions is larger than that in neat liquid water and vice versa for sodium chloride solutions. Such peculiar ion-specific behavior, called anomalous diffusion, is not reproduced in typical force field based molecular dynamics (MD) simulations due to inadequate treatment of ion-water interactions. Herein, this hurdle is tackled by using machine learned atomic potentials (MLPs) trained on data from density functional theory calculations. MLP based atomistic MD simulations of aqueous salt solutions reproduce experimentally determined thermodynamic, structural, dynamical, and transport properties, including their varied trends in water diffusivities across salt concentration. This enables an examination of their intermolecular structure to unravel the microscopic underpinnings of the differences in their transport properties. While both ions in CsI solutions contribute to the faster diffusion of water molecules, the competition between the heavy retardation by Na ions and the slight acceleration by Cl ions in NaCl solutions reduces their water diffusivity.

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics

The stability of rare earth element (REE) complexes plays a crucial role in quantitatively assessing their hydrothermal migration and transformation. However, reliable data are lacking under high-temperature hydrothermal conditions, which hampers our understanding of the association behavior of REE. Here a deep learning potential model for the LaCl3-H2O system in hydrothermal fluids is developed based on the first-principles density functional theory calculations. The model accurately predicts the radial distribution functions compared to ab initio molecular dynamics (AIMD) simulations. Furthermore, species of La-Cl complexes, the dissociation pathway of the La-Cl complexes dissociation process, and the potential of mean forces and corresponding association constants (logK) for LaCln3-n (n = 1-4) are extensively investigated under a wide range of temperatures and pressures. Empirical density models for logK calculation are fitted with these data and can accurately predict logK data from both experimental results and AIMD simulations. The distribution of La-Cl species is also evaluated across a wide range of temperatures, pressures, and initial chloride concentration conditions. The results show that La-Cl complexes are prone to forming in a low-density solution, and the number of bonded Cl- ions increases with rising temperature. In contrast, in a high-density solution, La3+ dominates and becomes the more prevalent species.

Cation coordination polyhedra lead to multiple lengthscale organization in aqueous electrolytes

Understanding multiple lengthscale correlations in the pair distribution functions (PDFs) of aq. electrolytes is a persistent challenge. Here, the coordination chemistry of polyoxoanions supports an ion-network of cation-coordination polyhedra in NaNO3(aq) and NaNO2(aq) that induce long-range solution structure. Oxygen correlations associated with Na+-coordination polyhedra have two characteristics lengthscales; 3.5-5.5 Å and 5.5-7.5 Å, the latter solely associated oligomers. The PDF contraction between 5.5-7.5 Å observed in many electrolytes is attributed to the distinct O⋯O correlation found in dimers and dimer subunits within oligomers.

Implementation of the meta-GGA exchange-correlation functional in numerical atomic orbital basis: With systematic testing on SCAN, rSCAN, and r2SCAN functionals

Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the exchange-correlation functional. By including the kinetic energy density τ, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better accuracy and flexibility while retaining the efficiency of semi-local functionals. For example, the strongly constrained and appropriately normed (SCAN) meta-GGA functional has been proven to yield accurate results for solid and molecular systems. We implement meta-GGA functionals with both numerical atomic orbitals and plane wave bases in the ABACUS package. Apart from the exchange-correlation potential, we also discuss the evaluation of force and stress. To validate our implementation, we perform finite-difference tests and convergence tests with the SCAN, rSCAN, and r2SCAN meta-GGA functionals. We further test water hexamers, weakly interacting molecules from the S22 dataset, as well as 13 semiconductors using the three functionals. The results show satisfactory agreement with previous calculations and available experimental values.

Why Dissolving Salt in Water Decreases Its Dielectric Permittivity

The dielectric permittivity of salt water decreases on dissolving more salt. For nearly a century, this phenomenon has been explained by invoking saturation in the dielectric response of the solvent water molecules. Herein, we employ an advanced deep neural network (DNN), built using data from density functional theory, to study the dielectric permittivity of sodium chloride solutions. Notably, the decrease in the dielectric permittivity as a function of concentration, computed using the DNN approach, agrees well with experiments. Detailed analysis of the computations reveals that the dominant effect, caused by the intrusion of ionic hydration shells into the solvent hydrogen-bond network, is the disruption of dipolar correlations among water molecules. Accordingly, the observed decrease in the dielectric permittivity is mostly due to increasing suppression of the collective response of solvent waters.

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Hydration of Alkali Metal and Halide Ions from Static and Dynamic Viewpoints

Ion hydration in aqueous solutions plays a paramount role in many fields. Despite many studies on ion hydration, the nature of ion hydration is not consistently understood at the molecular level. Combining neutron scattering (NS), wide-angle X-ray scattering (WAXS), and molecular dynamics (MD), we quantify the ionic hydration degree (hydration ability) systematically for a series of alkali metal and halide ions based on static and dynamic hydration numbers. The former is based on the orientational correlation of water molecules bound to an ion derived from the positional information from NS and WAXS. The latter is defined as the mean number of water molecules remaining in the first coordination shell of an ion over a residence time of bound water molecules around the ion from MD. The static and dynamic hydration numbers distinguish hydration from coordination and quantify the ionic hydration degree, which provides a valuable reference for understanding various phenomena in nature.

Microscopic Mechanism of Proton Transfer in Pure Water under Ambient Conditions

Water molecules and the associated proton transfer (PT) are prevalent in chemical and biological systems and have been a hot research topic. Spectroscopic characterization and ab initio molecular dynamics (AIMD) simulations have previously revealed insights into acidic and basic liquids. Presumably, the situation in the acidic/basic solution is not necessarily the same as in pure water; in addition, the autoionization constant for water is only 10^-14 under ambient conditions, making the study of PT in pure water challenging. To overcome this issue, we modeled periodic water box systems containing 1000 molecules for tens of nanoseconds based on a neural network potential (NNP) with quantum mechanical accuracy. The NNP was generated by training a dataset containing the energies and atomic forces of 17 075 configurations of periodic water box systems, and these data points were calculated at the MP2 level that considers electron correlation effects. We found that the size of the system and the duration of the simulation have a significant impact on the convergence of the results. With these factors considered, our simulations showed that hydronium (H₃O⁺) and hydroxide (OH^-) ions in water have distinct hydration structures, thermodynamic and kinetic properties, e.g., the longer-lasting and more stable hydrated structure of OH^- ions than that of H₃O⁺, as well as a significantly higher free energy barrier for the OH^--associated PT than that of H₃O⁺, leading the two to exhibit completely different PT behaviors. Given these characteristics, we further found that PT via OH^- ions tends not to occur multiple times or between many molecules. In contrast, PT via H₃O⁺ can synergistically occur among multiple molecules and prefers to adopt a cyclic pattern among three water molecules, while it occurs mostly in a chain pattern when more water molecules are involved. Therefore, our studies provide a detailed and solid microscopic explanation for the PT process in pure water.

Liquid Water: When Hyperpolarizability Fluctuations Boost and Reshape the Second Harmonic Scattering Intensities

Second harmonic scattering (SHS) is a method of choice to investigate the molecular structure of liquids. While a clear interpretation of SHS intensity exists for diluted solutions of dyes, the scattering due to solvents remains difficult to interpret quantitatively. Here, we report a quantum mechanics/molecular mechanics (QM/MM) approach to model the polarization-resolved SHS intensity of liquid water, quantifying different contributions to the signal. We point out that the molecular hyperpolarizability fluctuations and correlations cannot be neglected. The intermolecular orientational and hyperpolarizability correlations up to the third solvation layer strongly increase the scattering intensities and modulate the polarization-resolved oscillation that is predicted here by QM/MM without fitting parameters. Our approach can be generalized to other pure liquids to provide a quantitative interpretation of SHS intensities in terms of short-range molecular ordering.

Multifunctional Underwater Adhesive Film Enabled by a Single-Component Poly(ionic liquid)

Tremendous efforts have been devoted to exploiting synthetic wet adhesives for real-life applications. However, developing low-cost, robust, and multifunctional wet adhesive materials remains a considerable challenge. Herein, a wet adhesive composed of a single-component poly(ionic liquid) (PIL) that enables fast and robust underwater adhesion is reported. The PIL adhesive film possesses excellent stretchability and flexibility, enabling its anchoring on target substrates regardless of deformation and water scouring. Surface force measurements show the PIL can achieve a maximum adhesion of 56.7 mN·m^-1 on diverse substrates (both hydrophilic and hydrophobic substrates) in aqueous media, within ∼30 s after being applied. The adhesion mechanisms of the PIL were revealed via the force measurements, and its robust wet adhesive capacity was ascribed to the synergy of different non-covalent interactions, such as of hydrogen bonding, cation-π, electrostatic, and van der Waals interactions. Surprisingly, this PIL adhesive film exhibited impressive underwater sound absorption capacity. The absorption coefficient of a 0.7 mm-thick PIL film to 4-30 kHz sound waves could be as high as 0.80-0.92. This work reports a multifunctional PIL wet adhesive that has promising applications in many areas and provides deep insights into interfacial interaction mechanisms underlying the wet adhesion capability of PILs.

Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities

Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.

Scaled charges for ions: An improvement but not the final word for modeling electrolytes in water

In this work, we discuss the use of scaled charges when developing force fields for NaCl in water. We shall develop force fields for Na⁺ and Cl^- using the following values for the scaled charge (in electron units): ±0.75, ±0.80, ±0.85, and ±0.92 along with the TIP4P/2005 model of water (for which previous force fields were proposed for q = ±0.85 and q = ±1). The properties considered in this work are densities, structural properties, transport properties, surface tension, freezing point depression, and maximum in density. All the developed models were able to describe quite well the experimental values of the densities. Structural properties were well described by models with charges equal to or larger than ±0.85, surface tension by the charge ±0.92, maximum in density by the charge ±0.85, and transport properties by the charge ±0.75. The use of a scaled charge of ±0.75 is able to reproduce with high accuracy the viscosities and diffusion coefficients of NaCl solutions for the first time. We have also considered the case of KCl in water, and the results obtained were fully consistent with those of NaCl. There is no value of the scaled charge able to reproduce all the properties considered in this work. Although certainly scaled charges are not the final word in the development of force fields for electrolytes in water, its use may have some practical advantages. Certain values of the scaled charge could be the best option when the interest is to describe certain experimental properties.

Local Molecular Field Theory for Coulomb Interactions in Aqueous Solutions

Coulomb interactions play a crucial role in a wide array of processes in aqueous solutions but present conceptual and computational challenges to both theory and simulations. We review recent developments in an approach addressing these challenges─local molecular field (LMF) theory. LMF theory exploits an exact and physically suggestive separation of intermolecular Coulomb interactions into strong short-range and uniformly slowly varying long-range components. This allows us to accurately determine the averaged effects of the long-range components on the short-range structure using effective single particle fields and analytical corrections, greatly reducing the need for complex lattice summation techniques used in most standard approaches. The simplest use of these ideas in aqueous solutions leads to the short solvent (SS) model, where both solvent-solvent and solute-solvent Coulomb interactions have only short-range components. Here we use the SS model to give a simple description of pairing of nucleobases and biologically relevant ions in water.

Quantifying the Structure of Water and Hydrated Monovalent Ions by Density Functional Theory-Based Molecular Dynamics

The accurate description of the structures of water and hydrated ions is important in electrochemical desalination, ion separation, and supercapacitors. In this work, we present an ab initio atomistic simulation-based study to explore the structure of water and hydrated monovalent ions (Li⁺, Na⁺, K⁺, Rb⁺, F^-, and Cl^-) at ambient conditions using generalized gradient approximation (GGA)-based methods with and without van der Waals correction (PBE, PBE + D3, and revPBE + D3) and recently developed strongly constrained and appropriately normed (SCAN) meta-GGA. We find that both revPBE + D3 and SCAN can well capture the structure of bulk water with +30 K artificial high temperature in contrast to overstructuring water using PBE and PBE + D3. However, being the same as PBE + D3, revPBE + D3 overestimates the structure of the hydration shell, especially for monovalent cations. Surprisingly, SCAN can well match the experimental results of hydrated monovalent ions. Detailed structure analyzes of entropy reveal that the hydration shell under the level of PBE + D3 and revPBE + D3 is more disordered and looser than SCAN. The successful prediction of the flexible SCAN functional could facilitate the exploration of complex ionic processes in the aqueous phase, the interactions of hydrated ions with surfaces, and solvation states in nanopores at an accurate, efficient, predictive, and ab initio level.

Unveiling the structure of aqueous magnesium nitrate solutions by combining X-ray diffraction and theoretical calculations

The structure of aqueous magnesium nitrate solution is gaining significant interest among researchers, especially whether contact ion pairs exist in concentrated solutions. Here, combining X-ray diffraction experiments, quantum chemical calculations and ab initio molecular dynamics simulations, we report that the [Mg(NO₃)₂] molecular structure in solution from the coexistence of a free [Mg(H₂O)₆]²⁺ octahedral supramolecular structure with a free [NO₃(H₂O)_n]^- (n = 11-13) supramolecular structure to an [Mg²⁺(H₂O)_n(NO₃^-)_m] (n = 3, 4, 5; m = 3, 2, 1) associated structure with increasing concentration. Interestingly, two hydration modes of NO₃^--the nearest neighbor hydration with a hydration distance less than 3.9 Å and the next nearest neighbor hydration with hydration distance ranging from 3.9 to 4.3 Å-were distinguished. With an increase in the solution concentration, the hydrated NO₃^- ions lost outer layer water molecules, and the hexagonal octahedral hydration structure of [Mg(H₂O)₆²⁺] was destroyed, resulting in direct contact between Mg²⁺ and NO₃^- ions in a monodentate way. As the concentration of the solution further increased, NO₃^- ions replaced water molecules in the hydration layer of Mg²⁺ to form three-ion clusters and even more complex chains or linear ion clusters.

Dissolving salt is not equivalent to applying a pressure on water

Salt water is ubiquitous, playing crucial roles in geological and physiological processes. Despite centuries of investigations, whether or not water’s structure is drastically changed by dissolved ions is still debated. Based on density functional theory, we employ machine learning based molecular dynamics to model sodium chloride, potassium chloride, and sodium bromide solutions at different concentrations. The resulting reciprocal-space structure factors agree quantitatively with neutron diffraction data. Here we provide clear evidence that the ions in salt water do not distort the structure of water in the same way as neat water responds to elevated pressure. Rather, the computed structural changes are restricted to the ionic first solvation shells intruding into the hydrogen bond network, beyond which the oxygen radial-distribution function does not undergo major change relative to neat water. Our findings suggest that the widely cited pressure-like effect on the solvent in Hofmeister series ionic solutions should be carefully revisited.

By advanced machine learning techniques, first-principles simulations find that dissolving salt in water does not change water structure drastically. It is contrary to the notion of “pressure effect” which has been widely applied over past 25 years.