Ab initio thermodynamics of liquid and solid water.

Accurate and efficient simulation of liquids, such as water and salt solutions, using high-level wave function theories is still a formidable task for computational chemists owing to the high computational costs. In this study, we develop a deep machine learning potential based on fragment-based second-order Møller-Plesset perturbation theory (DP-MP2) for water through neural networks. We show that the DP-MP2 potential predicts the structural, dynamical, and thermodynamic properties of liquid water in better agreement with the experimental data than previous studies based on density functional theory (DFT). The nuclear quantum effects (NQEs) on the properties of liquid water are also examined, which are noticeable in affecting the structural and dynamical properties of liquid water under ambient conditions. This work provides a general framework for quantitative predictions of the properties of condensed-phase systems with the accuracy of high-level wave function theory while achieving significant computational savings compared to ab initio simulations.

Nowadays, although our understanding on liquid water have lots of progresses due to the development of experimental tools and computer simulation techniques, the molecular level structure of water, its heterogeneity, is still elusive. In the end of the nineteenth century, Rntgen proposed that the water is a mixture of two molecular complexes, which cannot be confirmed by experiments at that time. In the middle of the twentieth century, Bernal and his followers regarded the structure of liquid water as a random tetrahedral network, which was widely accepted by most scientists. With the development of computer science and the discovery of several amorphism, more and more attentions are paid on the mixture model of liquid water. In this paper, we firstly review some latest evidences about the multiple types of local structure in liquid water in both simulations and experiments. In all-atom simulation, the distributions of the local structure index obtained by minimizing the energy of samples are double peak at all temperatures. In experiment, the X-ray emission spectroscopy of liquid water at ambient pressure shows that there are two local structures in liquid water, one is order and ice-like, the other one is disorder and gas-like. Secondly, some results of our group on this topic are presented. We transformed the Raman spectra into the high-dimensional vectors and analyze the vectors with the principal component analysis method. The results show that all the end points of vectors are in a line in the high-dimensional space which implies that they can be obtained by linearly combining two basic points in that line. This means that the Raman spectra can be decomposed into two basic spectra. We also perform the same analysis on the distributions of tetrahedral order parameter in liquid water and obtained similar results. It is an obvious signal of the existence of multi-component in liquid water. Finally, we introduce the mixture model of liquid water which can be used to explain the thermodynamic properties of liquid water. In the mixture model, the form of the Gibbs free energy of liquid water is the same as the binary regular solution. The free energy is a function of the concentration of the disorder local structure. The anomalies of liquid water are directly caused by the change of concentration of the disorder local structure. In the low temperature and high pressure region, the mixture model has a critical point, which is consistent with the liquid-liquid phase transition theory.

Cryopreservation is the preservation and storage of biomaterialsusing low temperatures. There are several approaches to cryopreservation,and these often include the use of cryoprotectants, which are solutesused to lower the freezing point of water. Isochoric (constant-volume)cryopreservation is a form of cryopreservation that has been gaininginterest over the past 18 years. This method utilizes the anomalousnature of water in that it expands as it freezes. The expansion ofice on freezing is used to induce a pressure in the system that limitsice growth. In this work, we use Gibbsian thermodynamics, the Elliottet al. multisolute osmotic virial equation, the Feistel and Wagnercorrelation for ice Ih, and the Grenke and Elliott correlation forthe thermodynamic properties of liquid water at low temperatures andhigh pressures to predict how the pressure, volume fraction of ice,and solute concentration in the unfrozen fraction change as the solutionis cooled isochorically. We then verified our model by predictingexperimental results for saline solutions and ternary aqueous solutionscontaining NaCl and organic compounds commonly used as cryoprotectants:glycerol, ethylene glycol, propylene glycol, and dimethyl sulfoxide.We found that our model accurately predicts experimental data thatwere collected for cryoprotectant concentrations as high as 5 M, andtemperatures as low as −25 °C. Since we have shown thatour liquid water correlation, on which this work was based, makesaccurate predictions to −70 °C, as long as the pressureis not higher than 400 MPa, we anticipate that the prediction methodspresented in this work will be accurate down to −70 °C.In this work we also modeled how sealing the isochoric chamber atroom temperature versus at the nucleation temperature impacts isochoricfreezing. The prediction methods developed in this work can be usedin the future design of isochoric cryopreservation experiments andprotocols.

The random occupancy of hydrogen atoms or proton disorder is the most unique feature of ice crystal. Taking the cubic Ic ice as an example, we study the impact of proton disorder on various physical properties by first‐principles calculation in density‐functional theory for the first time. Our results show that the dielectric polarization enhances the cohesion of H2O molecules, thus increases the bulk modulus of Ic ice. The shear elastic constants are more sensitive to dielectric polarization than the elastic constant of normal strain. Ic ice is a semiconductor with a wide direct bandgap of 5.54 eV. The disorder of electric dipole moments raises the valence bands and decreases the energy bandgap. The average dielectric constant is less affected by the disorder. The present conclusions also keep valid for the more common hexagonal Ih ice based on its similar local structure with Ic ice. © 2012 Wiley Periodicals, Inc.

Evident in many physical and chemical phenomena, thermodynamics is the study of how energy is stored, transformed and transferred in a molecule or material. However, prediction of these properties with simulation techniques is a non-trivial task as several factors such as composition and intermolecular interactions come into play. While molecular dynamics and ab initio molecular dynamics are the most common techniques for the prediction of thermodynamic properties, there exists many shortcomings associated with their use. Therefore, in this work we instead apply QCE theory to predict the thermodynamic properties of liquid water. This theory assumes that a condensed phase system can be represented as a 'mixture' of varying sized clusters rather than as a continuum. As QCE theory relies on first-principle simulations and statistical thermodynamics to determine the thermodynamic behavior of a system, appropriate selection of clusters is a crucial step towards achieving accurate predictions. In this study, we use molecular dynamics and ab initio calculations to obtain initial configurations of 400 water clusters, Wn where n = 3 to 10 and contrast their stability using two different criteria. The role of entropy towards cluster stabilization is investigated by comparing the binding (ΔEBIND/mol) and Gibbs free binding energy per molecule (ΔGBIND/mol) of various Wn at 298.15 K. Initial clustersets are constructed by exploring two-, three-, four and five-combinations of clustersets using the minimum ΔGBIND/mol structures of Wn. We also expand the ΔGBIND/mol criteria for Wn of sizes 3 to 7 to include values larger than 0.0 kJ mol-1 and smaller than 3.0 kJ mol-1 as a means of improving thermodynamic predictions. 37 of the 459 resulting clustersets predicted the correct boiling point of water and its thermodynamic properties with an accuracy of 95%. A scaled population-weighted infrared spectrum was compared to experimental results to validate the composition of the top 5 clustersets. The predicted spectra showed an exact match within the low frequency range (<1000 cm-1) with some discrepancy at the high frequency range (>3400 cm-1). This work highlights that ΔGBIND/mol is so far the best criteria to apply when determining an appropriate clusterset for QCE theory.

Enhancing the design of supported metal–oxide catalysts, featuring metal particles dispersed on an oxide support, is essential for optimizing the performance of numerous industrial chemical processes. Advances in computational chemistry over the last few decades have had a great impact on design strategies for obtaining active, selective, and stable catalysts. This chapter outlines computational approaches for modeling metal–oxide catalytic systems at the atomic level, and reviews pertinent studies that exemplify these methods. Examples are chosen to emphasize both quantum-based methods [utilizing density functional theory (DFT) and ab initio thermodynamics] and classical force-field methods (utilizing the ReaxFF empirical potential). We discuss studies that use DFT to evaluate the relative energies of metal–oxide surface structures, studies that extend the formalism of DFT to non-zero temperature and pressure via ab initio thermodynamics, and finally studies that use the COMB and ReaxFF empirical force-fields in MD and MC simulations to investigate system dynamics and structure at large scales. Reviewing the application of these methods will provide the reader with a general understanding of how computational methods can be applied to atomistic studies of supported metal–oxide catalysts.

Due to the increased concern about energy and environmental issues, significant attention has been paid to the development of large-scale energy storage devices to facilitate the utilization of clean energy sources. The redox flow battery (RFB) is one of the most promising systems. Recently, the high cost of transition-metal complex-based RFB has promoted the development of aqueous RFBs with redox-active organic molecules. To expand the working voltage, computational chemistry has been applied to search for organic molecules with lower or higher redox potentials. However, redox potential computation based on implicit solvation models would be challenging due to difficulty in parametrization when considering the complex solvation of supporting electrolytes. Besides, although ab initio molecular dynamics (AIMD) describes the supporting electrolytes with the same level of electronic structure theory as the redox couple, the application is impeded by the high computation costs. Recently, machine learning molecular dynamics (MLMD) has been illustrated to accelerate AIMD by several orders of magnitude without sacrificing the accuracy. It has been established that redox potentials can be computed by MLMD with two separated machine learning potentials (MLPs) for reactant and product states, which is redundant and inefficient. In this work, an automated workflow is developed to construct a universal MLP for both states, which can compute the redox potentials or acidity constants of redox-active organic molecules more efficiently. Furthermore, the predicted redox potentials can be evaluated at the hybrid functional level with much lower costs, which would facilitate the design of aqueous organic RFBs.

The NNL.Fe.qSNAP-ZBL.2024.1 machine-learned potential (MLP) has been generated to support the development of an elemental body-centered cubic (BCC) Fe athermal recombination corrected neutron damage model and simulations of primary recoil atom (PRA) cascades in BCC Fe. This MLP is a quadratic spectral neighbor analysis potential (qSNAP) hybridized with the universal Ziegler-Beirsack-Littmark (ZBL) potential at short-range and is named according to Naval Nuclear Laboratory MLP naming conventions (NNL.material-system.MLP-type.year.version). Training set calculations for Fe are presented along with the subsequent MLP fitting procedure. A key criterion of the fitting procedure is that ZBL describes the short-range interaction with minimal impact on the MLP. The MLP is compared to density functional theory (DFT) predicted properties relevant to radiation damage simulation, including threshold displacement energies, for validation. The NNL.Fe.qSNAP-ZBL.2024.1 potential is considered suitable for molecular dynamics (MD) simulations of radiation defects up to 800 K and PRA cascades in BCC Fe up to around 10 keV. The potential can additionally be used on a limited basis for recoils of 10–20 keV, within which range the emergence of structures outside the training set in cascade simulations may cause system instabilities.

103Pd and 125I brachytherapy sources are being used for interstitial implants in tumor sites such as the prostate. Recently, a double-wall 103Pd source has been introduced, which has a design different from that of sources presently on the market. Dosimetric characteristics (dose rate constant, radial dose function, and anisotropy function) of this source were experimentally and theoretically determined following the AAPM Task Group 43 recommendations and were related to the October 10, 2000 revision of the NIST 1999 SK Standard for 103Pd. Measurements were performed in a Solid Water phantom using LiF thermoluminescent dosimeters. For these measurements, slabs of Solid Water phantom material were machined to accommodate the source and LiF TLD chips of dimensions (3.1 x 3.1 x 0.8 mm3) and (1.0 x 1.0 x 1.0 mm3). The TLD chips were surrounded by at least 10 cm of Solid Water phantom material to provide full scattering conditions. The Monte Carlo simulations were performed in Solid Water and liquid water using the PTRAN code. The results of this investigation show an excellent agreement (within 5%) between the measured (0.67+/-8% cGy h(-1) U(-1)) and calculated (to be 0.65+/-3% cGy h(-1) U(-1)) dose rate constant in Solid Water. The Monte Carlo calculated dose rate constant of the Best 103Pd in water was found to be 0.67+/-0.02 cGy h(-1) U(-1). The radial dose function, g(r), of the new 103Pd source was measured at distances ranging from 0.5 and 7 cm using LiF TLD in Solid Water phantom material. Moreover, the radial dose function of the new source was calculated in liquid water and Solid Water at distances ranging from 0.1 to 7 cm using the PTRAN Monte Carlo Code. The anisotropy function, F(r, theta), of the new 103Pd source was also measured in Solid Water and calculated in both Solid Water and water phantom material. From the anisotropy functions, the anisotropy factors, and anisotropy constant were calculated for each medium. The results indicated that the measured anisotropy constant of the Best 103Pd source in Solid Water was 0.89+/-5%. Complete dosimetric data are described in this manuscript.

The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily available for cluster data but effectively out of scope for periodic structures. We show that local, atom-centered descriptors for machine-learned potentials enable the prediction of bulk properties from cluster model training data, agreeing reasonably well with predictions from bulk training data. We demonstrate such transferability by studying structural and dynamical properties of bulk liquid water with density functional theory and have found an excellent agreement with experimental and theoretical counterparts.

Singularity of liquid water-data analysis and modelling

The structural and thermodynamic properties of liquid water and of the dilute solutions of methane and ethane in water were calculated by Monte Carlo simulations in the temperature range 298 K to 318 K and 298 K to 333 K, respectively. The nonpolar molecules were modeled as one- and two-center Lennard–Jones particles; for the interaction potential of water a modified TIP5P model was used. The results indicate that the nonpolar solutes tend to aggregate with increasing temperature. Methane molecules preferably form water-separated pairs, even at higher temperatures, whereas for ethane contact pairs are more likely. For the thermodynamic conditions studied here, the residual chemical potential of water is a linear function of temperature.

Coarse-grained water models offer opportunities to study the molecular processes in water on spatial and temporal scales inaccessible to conventional atomic-level simulations. Such a model that can accurately describe the structural, electrostatic and thermodynamic properties of liquid water is difficult to derive but invaluable. Here, we present FlexDQ, a flexible three-site coarse-grained model, representing clusters of water molecules. This model was derived using a full bottom-up approach in which atomistic information of water molecules is processed using clustering techniques based on considerations of both energy and geometry and a mapping algorithm that encodes information of dipole and quadrupole moments of water clusters into coarsely grained interaction sites. Parameterization of FlexDQ was conducted consistently and systematically, augmented by these data. The resulting model reproduces not only dipolar and quadrupolar fluctuations of water clusters, but also their local structural correlations in the bulk phase and, to a good extent, pressure-related properties, dielectric permittivity and water-air interfacial properties that were not targeted for parameterization. The ability of our model to reproduce various properties of water has been shown to originate from its explicit treatment of electrostatics. Coupled with the popular MARTINI CG model, FlexDQ captures the entropy-enthalpy balance of hydrophobic interactions and describes the dielectric behavior of water at the water-membrane interface, yielding a positive internal electrostatic potential in membranes, which is otherwise difficult to reproduce using MARTINI alone. Together, the FlexDQ model and its development framework reported here show promise for studies on the biomolecules in water.

Computing dissipative particle dynamics interactions to render molecular structure and temperature-dependent properties of simple liquids

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.