Dynamical Properties Of Liquid Water Research Articles

Learning a reactive potential for silica-water through uncertainty attribution

The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential trained on PaiNN architecture can accurately capture silicate-water reactivity. The model was trained on a dataset comprising 400,000 energies and forces of molecular clusters at the ωB97X-D3/def2-TZVP level. To ensure the robustness of the model, we introduce a general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. The potential reproduces static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.

Entropy of Liquid Water as Predicted by the Two-Phase Thermodynamic Model and Data-Driven Many-Body Potentials.

We investigate the entropy of liquid water at ambient conditions using the two-phase thermodynamic (2PT) model, applied to both common pairwise-additive water models and the MB-pol and MB-pol(2023) data-driven many-body potentials. Our simulations demonstrate that the 2PT model yields entropy values in semiquantitative agreement with experimental data when using MB-pol and MB-pol(2023). Additionally, our analyses indicate that the entropy values predicted by pairwise-additive water models may benefit from error compensation between the inherent approximations of the 2PT model and the known limitations of these water models in describing many-body interactions. Despite its approximate nature, the simplicity of the 2PT model makes it a valuable tool for estimating relative entropy changes of liquid water across various environments, especially when combined with water models that provide a consistently robust representation of the structural, thermodynamic, and dynamical properties of liquid water.

Simple Model of Liquid Water Dynamics.

We develop an analytical statistical-mechanical model to study the dynamic properties of liquid water. In this two-dimensional model, neighboring waters can interact through a hydrogen bond, a van der Waals contact, or an ice-like cage structure or have no interaction. We calculate the diffusion coefficient, viscosity, and thermal conductivity versus temperature and pressure. The trends follow those seen in the water experiments. The model explains that in warm water, heating drives faster diffusion but less interaction, so the viscosity and conductivity decrease. Cooling cold water causes poorer energy exchange because water's ice-like cages are big and immobile and collide infrequently. The main antagonism in water dynamics is not between vdW and H bonds, but it is an interplay between both those pair interactions, multibody cages, and no interaction. The value of this simple model is that it is analytical, so calculations are immediate, and it gives interpretations based on molecular physics.

Transport properties in liquids from first-principles: The case of liquid water and liquid argon.

Shear and bulk viscosities of liquid water and argon are evaluated from first-principles in the density functional theory (DFT) framework, by performing molecular dynamics simulations in the NVE ensemble and using the Kubo-Greenwood equilibrium approach. The standard DFT functional is corrected in such a way to allow for a reasonable description of van der Waals effects. For liquid argon, the thermal conductivity has been also calculated. Concerning liquid water, to our knowledge, this is the first estimate of the bulk viscosity and of the shear-viscosity/bulk-viscosity ratio from first-principles. By analyzing our results, we can conclude that our first-principles simulations, performed at a nominal average temperature of 366 to guarantee that the systems are liquid-like, actually describe the basic dynamical properties of liquid water at about 330K. In comparison with liquid water, the normal, monatomic liquid Ar is characterized by a much smaller bulk-viscosity/shear-viscosity ratio (close to unity) and this feature is well reproduced by our first-principles approach, which predicts a value of the ratio in better agreement with experimental reference data than that obtained using the empirical Lennard-Jones potential. The computed thermal conductivity of liquid argon is also in good agreement with the experimental value.

Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks.

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.

Nuclear Quantum Effect and Its Temperature Dependence in Liquid Water from Random Phase Approximation via Artificial Neural Network.

We report structural and dynamical properties of liquid water described by the random phase approximation (RPA) correlation together with the exact exchange energy (EXX) within density functional theory. By utilizing thermostated ring polymer molecular dynamics, we examine the nuclear quantum effects and their temperature dependence. We circumvent the computational limitation of performing direct first-principles molecular dynamics simulation at this high level of electronic structure theory by adapting an artificial neural network model. We show that the EXX+RPA level of theory accurately describes liquid water in terms of both dynamical and structural properties.

Ab initio molecular dynamics simulation of liquid water with fragment-based quantum mechanical approach under periodic boundary conditions

In this study, we investigated the structural and dynamical properties of liquid water by using ab initio molecular dynamics simulation under periodic boundary conditions based on the fragment-based quantum mechanical approach. This study was carried out using the second-order Møller-Plesset perturbation theory (MP2) with the aug-cc-pVDZ basis set, which has been validated to be sufficiently accurate for describing water interactions. Diverse properties of liquid water, including radial distribution functions, diffusion coefficient, dipole moment, triplet oxygen-oxygen-oxygen angles, and hydrogen-bond structures, were simulated. This ab initio description leads to these properties in good agreement with experimental observations. This computational approach is general and transferable, providing a comprehensive framework for ab initio predictions of properties of condensed-phase matters.

Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional.

We investigate the temperature dependence of nuclear quantum effects (NQEs) on structural and dynamic properties of liquid water by training a neural network force field using first-principles molecular dynamics (FPMD) based on the strongly constrained and appropriately normed meta-generalized gradient approximation exchange-correlation approximation. The FPMD simulation based on density functional theory has become a powerful computational approach for studying a wide range of condensed phase systems. However, its large computational cost makes it difficult to incorporate NQEs in the simulation and investigate temperature dependence of various properties. To circumvent this difficulty, we use an artificial neural network model and employ the thermostatted ring polymer MD approach for studying the temperature dependence of NQEs on various properties. The NQEs generally bring the radial distribution functions closer to the experimental measurements. Translational diffusivity and rotational dynamics of water molecules are both slowed down by the NQEs. The competing inter-molecular and intra-molecular quantum effects on hydrogen bonds, as discussed by Habershon, Markland, and Manolopoulos [J. Chem. Phys. 131(2), 024501 (2019)], can explain the observed temperature dependence of the NQEs on the dynamical properties in our simulation.

Molecular Dynamics of Open Systems: Construction of a Mean‐Field Particle Reservoir

AbstractThe simulation of open molecular systems requires explicit or implicit reservoirs of energy and particles. Whereas full atomistic resolution is desired in the region of interest, there is some freedom in the implementation of the reservoirs. Here, a combined, explicit reservoir is constructed by interfacing the atomistic region with regions of point‐like, non‐interacting particles (tracers) embedded in a thermodynamic mean field. The tracer molecules acquire atomistic resolution upon entering the atomistic region and equilibrate with this environment, while atomistic molecules become tracers governed by an effective mean‐field potential after crossing the atomistic boundary. The approach is extensively tested on thermodynamic, structural, and dynamic properties of liquid water. Conceptual and numerical advantages of the procedure as well as new perspectives are highlighted and discussed.

Force Field for Water Based on Neural Network.

We developed a novel neural network-based force field for water based on training with high-level ab initio theory. The force field was built based on an electrostatically embedded many-body expansion method truncated at binary interactions. The many-body expansion method is a common strategy to partition the total Hamiltonian of large systems into a hierarchy of few-body terms. Neural networks were trained to represent electrostatically embedded one-body and two-body interactions, which require as input only one and two water molecule calculations at the level of ab initio electronic structure method CCSD/aug-cc-pVDZ embedded in the molecular mechanics water environment, making it efficient as a general force field construction approach. Structural and dynamic properties of liquid water calculated with our force field show good agreement with experimental results. We constructed two sets of neural network based force fields: nonpolarizable and polarizable force fields. Simulation results show that the nonpolarizable force field using fixed TIP3P charges has already behaved well, since polarization effects and many-body effects are implicitly included due to the electrostatic embedding scheme. Our results demonstrate that the electrostatically embedded many-body expansion combined with neural network provides a promising and systematic way to build next-generation force fields at high accuracy and low computational costs, especially for large systems.

Structural, electronic, and dynamical properties of liquid water by ab initio molecular dynamics based on SCAN functional within the canonical ensemble

We perform ab initio molecular dynamics (AIMD) simulation of liquid water in the canonical ensemble at ambient conditions using the strongly constrained and appropriately normed (SCAN) meta-generalized-gradient approximation (GGA) functional approximation and carry out systematic comparisons with the results obtained from the GGA-level Perdew-Burke-Ernzerhof (PBE) functional and Tkatchenko-Scheffler van der Waals (vdW) dispersion correction inclusive PBE functional. We analyze various properties of liquid water including radial distribution functions, oxygen-oxygen-oxygen triplet angular distribution, tetrahedrality, hydrogen bonds, diffusion coefficients, ring statistics, density of states, band gaps, and dipole moments. We find that the SCAN functional is generally more accurate than the other two functionals for liquid water by not only capturing the intermediate-range vdW interactions but also mitigating the overly strong hydrogen bonds prescribed in PBE simulations. We also compare the results of SCAN-based AIMD simulations in the canonical and isothermal-isobaric ensembles. Our results suggest that SCAN provides a reliable description for most structural, electronic, and dynamical properties in liquid water.

Hydrogen-bond structure dynamics in bulk water: insights from ab initio simulations with coupled cluster theory.

An accurate and efficient ab initio molecular dynamics (AIMD) simulation of liquid water was made possible using the fragment-based approach (J. F. Liu, X. He and J. Z. H. Zhang, Phys. Chem. Chem. Phys., 2017, 19, 11931-11936). In this study, we advance the AIMD simulations using the fragment-based coupled cluster (CC) theory, more accurately revealing the structural and dynamical properties of liquid water under ambient conditions. The results show that the double-donor hydrogen-bond configurations in liquid water are nearly in balance with the single-donor configurations, with a slight bias towards the former. Our observation is in contrast to the traditional tetrahedral water structure. The hydrogen-bond switching dynamics in liquid water are very fast, with a hydrogen-bond life time of around 0.78 picoseconds, determined using AIMD simulation at the CCD/aug-cc-pVDZ level. This time scale is remarkably shorter than the ∼3.0 picoseconds that is commonly obtained from traditional nonpolarized force fields and density functional theory (DFT) based first-principles simulations. Additionally, the obtained radial distribution functions, triplet oxygen angular distribution, diffusion coefficient, and the dipole moment of the water molecule are uniformly in good agreement with the experimental observations. The current high-level AIMD simulation sheds light on the understanding of the structural and dynamical properties of liquid water.

Ab-Initio Molecular Dynamics Simulations and Calculations of Spectroscopic Parameters in Hydrogen-Bonding Liquids in Confinement (Project 8)

Abstract We investigate the effect of several nanoscale confinements on structural and dynamical properties of liquid water and binary aqueous mixtures. By means of molecular dynamics simulations based on density functional theory and atomistic force fields. Our main focus is on the dependence on the structure and the hydrogen-bonding-network of the liquids near the confinement interface at atomistic resolution. As a complementary aspect, spatially resolved profiles of the proton NMR chemical shift values are used to quantify the local strength of the hydrogen-bond-network.

Dynamics of vibrational spectral diffusion in water: Effects of dispersion interactions, temperature, density, system size and fictitious orbital mass

We have carried out a series of ab initio molecular dynamics simulations to investigate the effects of dispersion interactions, temperature, density, system size and fictitious orbital mass on the dynamics of vibrational spectral diffusion and other dynamical properties of liquid water. While most of the simulations are performed at room temperature, the effects of temperature are looked at by carrying out additional simulations at 25K above the reported melting point of ab initio water for our chosen density functionals. Apart from the dynamical properties, we have also looked at some of the equilibrium properties such as structure, frequency distributions and frequency-structure correlations for all the systems. In general, it is found that the inclusion of dispersion interactions makes the dynamics faster. The effects of system size, orbital mass and temperature are also discussed for the systems considered here.

Ab initio theory and modeling of water

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 Ih 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.

Structure and dynamics of aqueous solutions from PBE-based first-principles molecular dynamics simulations.

Establishing an accurate and predictive computational framework for the description of complex aqueous solutions is an ongoing challenge for density functional theory based first-principles molecular dynamics (FPMD) simulations. In this context, important advances have been made in recent years, including the development of sophisticated exchange-correlation functionals. On the other hand, simulations based on simple generalized gradient approximation (GGA) functionals remain an active field, particularly in the study of complex aqueous solutions due to a good balance between the accuracy, computational expense, and the applicability to a wide range of systems. Such simulations are often performed at elevated temperatures to artificially "correct" for GGA inaccuracies in the description of liquid water; however, a detailed understanding of how the choice of temperature affects the structure and dynamics of other components, such as solvated ions, is largely unknown. To address this question, we carried out a series of FPMD simulations at temperatures ranging from 300 to 460 K for liquid water and three representative aqueous solutions containing solvated Na+, K+, and Cl- ions. We show that simulations at 390-400 K with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional yield water structure and dynamics in good agreement with experiments at ambient conditions. Simultaneously, this computational setup provides ion solvation structures and ion effects on water dynamics consistent with experiments. Our results suggest that an elevated temperature around 390-400 K with the PBE functional can be used for the description of structural and dynamical properties of liquid water and complex solutions with solvated ions at ambient conditions.

Quantum ring-polymer contraction method: Including nuclear quantum effects at no additional computational cost in comparison to ab initio molecular dynamics.

We present a simple and accurate computational method which facilitates ab initio path-integral molecular dynamics simulations, where the quantum-mechanical nature of the nuclei is explicitly taken into account, at essentially no additional computational cost in comparison to the corresponding calculation using classical nuclei. The predictive power of the proposed quantum ring-polymer contraction method is demonstrated by computing various static and dynamic properties of liquid water at ambient conditions using density functional theory. This development will enable routine inclusion of nuclear quantum effects in ab initio molecular dynamics simulations of condensed-phase systems.

Infrared absorption spectra of liquid water and deuterated water by Car-Parrinello molecular dynamics

Infrared (IR) absorption spectra plays a key role in the study of thermodynamic and dynamic properties of liquid water. Car-Parrinello molecular dynamics simulations within the framework of density-functional theory are used to study the IR absorption, dielectric function and optical properties of liquid water and deuterated water. Using the energy minimization model to obtain the input structure, we calculate the molecular dipole moment of water and deuterated water at ambient temperature, and then obtain the IR absorption spectra by Fourier transforming the autocorrelated function of dipole moment. As the numerical analysis indicates, the calculated results fit well with previous literature experiments. The absorption peak represents the stretching mode of oxygen-hydrogen covalent bond and shows a slight red shift. The dielectric functions and optical constants of water and deuterated water at the temperature of 303 K are extracted from IR absorption spectrum.

Probing the structural and dynamical properties of liquid water with models including non-local electron correlation.

Water is a ubiquitous liquid that displays a wide range of anomalous properties and has a delicate structure that challenges experiment and simulation alike. The various intermolecular interactions that play an important role, such as repulsion, polarization, hydrogen bonding, and van der Waals interactions, are often difficult to reproduce faithfully in atomistic models. Here, electronic structure theories including all these interactions at equal footing, which requires the inclusion of non-local electron correlation, are used to describe structure and dynamics of bulk liquid water. Isobaric-isothermal (NpT) ensemble simulations based on the Random Phase Approximation (RPA) yield excellent density (0.994 g/ml) and fair radial distribution functions, while various other density functional approximations produce scattered results (0.8-1.2 g/ml). Molecular dynamics simulation in the microcanonical (NVE) ensemble based on Møller-Plesset perturbation theory (MP2) yields dynamical properties in the condensed phase, namely, the infrared spectrum and diffusion constant. At the MP2 and RPA levels of theory, ice is correctly predicted to float on water, resolving one of the anomalies as resulting from a delicate balance between van der Waals and hydrogen bonding interactions. For several properties, obtaining quantitative agreement with experiment requires correction for nuclear quantum effects (NQEs), highlighting their importance, for structure, dynamics, and electronic properties. A computed NQE shift of 0.6 eV for the band gap and absorption spectrum illustrates the latter. Giving access to both structure and dynamics of condensed phase systems, non-local electron correlation will increasingly be used to study systems where weak interactions are of paramount importance.

Nuclear quantum effects in liquid water from path-integral simulations using an ab initio force-matching approach

We have applied path integral simulations, in combination with new ab initio based water potentials, to investigate nuclear quantum effects in liquid water. Because direct ab initio path integral simulations are computationally expensive, a flexible water model is parameterised by force-matching to density functional theory-based molecular dynamics simulations. Static and dynamic properties of liquid water at ambient conditions are presented and the role of nuclear quantum effects, exchange-correlation functionals and dispersion corrections are discussed in regards to reproducing the experimental properties of liquid water.