Exploring Electrostatic Effects on the Hydrogen Bond Network of Liquid Water through Many-Body Molecular Dynamics

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.

Flow through Deformed Carbon Nanotubes Predicted by Rigid and Flexible Water Models

In this study, using nonequilibrium molecular dynamics simulation, the flow of water in deformed carbon nanotubes is studied for two water models TIP4P/2005 and simple point charge/FH (SPC/FH). The results demonstrated a nonuniform dependence of the flow on the tube deformation and the flexibility imposed on the water molecules, leading to an unexpected increase in the flow in some cases. The effects of the tube diameter and pressure gradient are investigated to explain the abnormal flow behavior with different degrees of structural deformation.

MBX: A many-body energy and force calculator for data-driven many-body simulations

Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the "many-body energy" (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.

Hydration Structure of Na+ and K+ Ions in Solution Predicted by Data-Driven Many-Body Potentials

The hydration structure of Na⁺ and K⁺ ions in solution is systematically investigated using a hierarchy of molecular models that progressively include more accurate representations of many-body interactions. We found that a conventional empirical pairwise additive force field that is commonly used in biomolecular simulations is unable to reproduce the extended X-ray absorption fine structure (EXAFS) spectra for both ions. In contrast, progressive inclusion of many-body effects rigorously derived from the many-body expansion of the energy allows the MB-nrg potential energy functions (PEFs) to achieve nearly quantitative agreement with the experimental EXAFS spectra, thus enabling the development of a molecular-level picture of the hydration structure of both Na⁺ and K⁺ in solution. Since the MB-nrg PEFs have already been shown to accurately describe isomeric equilibria and vibrational spectra of small ion-water clusters in the gas phase, the present study demonstrates that the MB-nrg PEFs effectively represent the long-sought-after models able to correctly predict the properties of ionic aqueous systems from the gas to the liquid phase, which has so far remained elusive.

Data-Driven Many-Body Potential Energy Functions for Generic Molecules: Linear Alkanes as a Proof-of-Concept Application

We present a generalization of the many-body energy (MB-nrg) theoretical/computational framework that enables the development of data-driven potential energy functions (PEFs) for generic covalently bonded molecules, with arbitrary quantum mechanical accuracy. The "nearsightedness of electronic matter" is exploited to define monomers as "natural building blocks" on the basis of their distinct chemical identity. The energy of generic molecules is then expressed as a sum of individual many-body energies of incrementally larger subsystems. The MB-nrg PEFs represent the low-order n-body energies, with n = 1-4, using permutationally invariant polynomials derived from electronic structure data carried out at an arbitrary quantum mechanical level of theory, while all higher-order n-body terms (n > 4) are represented by a classical many-body polarization term. As a proof-of-concept application of the general MB-nrg framework, we present MB-nrg PEFs for linear alkanes. The MB-nrg PEFs are shown to accurately reproduce reference energies, harmonic frequencies, and potential energy scans of alkanes, independently of their length. Since, by construction, the MB-nrg framework introduced here can be applied to generic covalently bonded molecules, we envision future computer simulations of complex molecular systems using data-driven MB-nrg PEFs, with arbitrary quantum mechanical accuracy.

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.

Uptake of N2O5 by aqueous aerosol unveiled using chemically accurate many-body potentials

The reactive uptake of N₂O₅ to aqueous aerosol is a major loss channel for nitrogen oxides in the troposphere. Despite its importance, a quantitative picture of the uptake mechanism is missing. Here we use molecular dynamics simulations with a data-driven many-body model of coupled-cluster accuracy to quantify thermodynamics and kinetics of solvation and adsorption of N₂O₅ in water. The free energy profile highlights that N₂O₅ is selectively adsorbed to the liquid–vapor interface and weakly solvated. Accommodation into bulk water occurs slowly, competing with evaporation upon adsorption from gas phase. Leveraging the quantitative accuracy of the model, we parameterize and solve a reaction–diffusion equation to determine hydrolysis rates consistent with experimental observations. We find a short reaction–diffusion length, indicating that the uptake is dominated by interfacial features. The parameters deduced here, including solubility, accommodation coefficient, and hydrolysis rate, afford a foundation for which to consider the reactive loss of N₂O₅ in more complex solutions.

The reactive uptake of N₂O₅ to aqueous aerosol is a major loss channel for nitrogen oxides in the troposphere. Here authors report a theoretical investigation on the N₂O₅ uptake into aqueous aerosol and determine the hydrolysis rates by numerically solving a molecularly detailed reaction–diffusion equation.

Machine Learning Approach for Describing Water OH Stretch Vibrations

A machine learning approach employing neural networks is developed to calculate the vibrational frequency shifts and transition dipole moments of the symmetric and antisymmetric OH stretch vibrations of a water molecule surrounded by water molecules. We employed the atom-centered symmetry functions (ACSFs), polynomial functions, and Gaussian-type orbital-based density vectors as descriptor functions and compared their performances in predicting vibrational frequency shifts using the trained neural networks. The ACSFs perform best in modeling the frequency shifts of the OH stretch vibration of water among the types of descriptor functions considered in this paper. However, the differences in performance among these three descriptors are not significant. We also tried a feature selection method called CUR matrix decomposition to assess the importance and leverage of the individual functions in the set of selected descriptor functions. We found that a significant number of those functions included in the set of descriptor functions give redundant information in describing the configuration of the water system. We here show that the predicted vibrational frequency shifts by trained neural networks successfully describe the solvent-solute interaction-induced fluctuations of OH stretch frequencies.

MB-Fit: Software infrastructure for data-driven many-body potential energy functions

Many-body potential energy functions (MB-PEFs), which integrate data-driven representations of many-body short-range quantum mechanical interactions with physics-based representations of many-body polarization and long-range interactions, have recently been shown to provide high accuracy in the description of molecular interactions from the gas to the condensed phase. Here, we present MB-Fit, a software infrastructure for the automated development of MB-PEFs for generic molecules within the TTM-nrg (Thole-type model energy) and MB-nrg (many-body energy) theoretical frameworks. Besides providing all the necessary computational tools for generating TTM-nrg and MB-nrg PEFs, MB-Fit provides a seamless interface with the MBX software, a many-body energy and force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, particularly within the MB-nrg framework, we believe that MB-Fit will enable routine predictive computer simulations of generic (small) molecules in the gas, liquid, and solid phases, including, but not limited to, the modeling of quantum isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams.

Data-driven many-body models enable a quantitative description of chloride hydration from clusters to bulk

We present a new data-driven potential energy function (PEF) describing chloride-water interactions, which is developed within the many-body-energy (MB-nrg) theoretical framework. Besides quantitatively reproducing low-order many-body energy contributions, the new MB-nrg PEF is able to correctly predict the interaction energies of small chloride-water clusters calculated at the coupled cluster level of theory. Importantly, classical and quantum molecular dynamics simulations of a single chloride ion in water demonstrate that the new MB-nrg PEF predicts x-ray spectra in close agreement with the experimental results. Comparisons with an popular empirical model and a polarizable PEF emphasize the importance of an accurate representation of short-range many-body effect while demonstrating that pairwise additive representations of chloride-water and water-water interactions are inadequate for correctly representing the hydration structure of chloride in both gas-phase clusters and solution. We believe that the analyses presented in this study provide additional evidence for the accuracy and predictive ability of the MB-nrg PEFs, which can then enable more realistic simulations of ionic aqueous systems in different environments.

Relationship between Hydrogen-Bonding Motifs and the 1b1 Splitting in the X-ray Emission Spectrum of Liquid Water

The split of the 1b₁ peak observed in the X-ray emission (XE) spectrum of liquid water has been the focus of intense research. Although several hypotheses have been proposed to explain the origin of this split, a consensus has not yet been reached. Here, we introduce a novel theoretical/computation approach which, combining path-integral molecular dynamics simulations with the MB-pol model and time-dependent density functional theory calculations, predicts the 1b₁ splitting in liquid water and not in crystalline ice, in agreement with the experimental observations. A systematic analysis of the underlying local structure of liquid water at ambient conditions indicates that several different hydrogen-bonding motifs contribute to the overall XE line shape in the energy range corresponding to emissions from the 1b₁ orbitals. This suggests that it is not possible to unambiguously attribute the split of the 1b₁ peak to only two specific structural arrangements of the underlying hydrogen-bonding network.

Preordering of water is not needed for ice recognition by hyperactive antifreeze proteins

Antifreeze proteins (AFPs) inhibit ice growth in organisms living in cold environments. Hyperactive insect AFPs are particularly effective, binding ice through "anchored clathrate" motifs. It has been hypothesized that the binding of hyperactive AFPs to ice is facilitated by preordering of water at the ice-binding site (IBS) of the protein in solution. The antifreeze protein TmAFP displays the best matching of its binding site to ice, making it the optimal candidate to develop ice-like order in solution. Here we use multiresolution simulations to unravel the mechanism by which TmAFP recognizes and binds ice. We find that water at the IBS of the antifreeze protein in solution does not acquire ice-like or anchored clathrate-like order. Ice recognition occurs by slow diffusion of the protein to achieve the proper orientation with respect to the ice surface, followed by fast collective organization of the hydration water at the IBS to form an anchored clathrate motif that latches the protein to the ice surface. The simulations suggest that anchored clathrate order could develop on the large ice-binding surfaces of aggregates of ice-nucleating proteins (INP). We compute the infrared and Raman spectra of water in the anchored clathrate motif. The signatures of the OH stretch of water in the anchored clathrate motif can be distinguished from those of bulk liquid in the Raman spectra, but not in the infrared spectra. We thus suggest that Raman spectroscopy may be used to probe the anchored clathrate order at the ice-binding surface of INP aggregates.

Molecular Origin of the Vibrational Structure of Ice Ih

An unambiguous assignment of the vibrational spectra of ice I_h remains a matter of debate. This study demonstrates that an accurate representation of many-body interactions between water molecules, combined with an explicit treatment of nuclear quantum effects through many-body molecular dynamics (MB-MD), leads to a unified interpretation of the vibrational spectra of ice I_h in terms of the structure and dynamics of the underlying hydrogen-bond network. All features of the infrared and Raman spectra in the OH stretching region can be unambiguously assigned by taking into account both the symmetry and the delocalized nature of the lattice vibrations as well as the local electrostatic environment experienced by each water molecule within the crystal. The high level of agreement with experiment raises prospects for predictive MB-MD simulations that, complementing analogous measurements, will provide molecular-level insights into fundamental processes taking place in bulk ice and on ice surfaces under different thermodynamic conditions.

Getting the Right Answers for the Right Reasons: Toward Predictive Molecular Simulations of Water with Many-Body Potential Energy Functions

The central role played by water in fundamental processes relevant to different disciplines, including chemistry, physics, biology, materials science, geology, and climate research, cannot be overemphasized. It is thus not surprising that, since the pioneering work by Stillinger and Rahman, many theoretical and computational studies have attempted to develop a microscopic description of the unique properties of water under different thermodynamic conditions. Consequently, numerous molecular models based on either molecular mechanics or ab initio approaches have been proposed over the years. However, despite continued progress, the correct prediction of the properties of water from small gas-phase clusters to the liquid phase and ice through a single molecular model remains challenging. To large extent, this is due to the difficulties encountered in the accurate modeling of the underlying hydrogen-bond network in which both number and strength of the hydrogen bonds vary continuously as a result of a subtle interplay between energetic, entropic, and nuclear quantum effects. In the past decade, the development of efficient algorithms for correlated electronic structure calculations of small molecular complexes, accompanied by tremendous progress in the analytical representation of multidimensional potential energy surfaces, opened the doors to the design of highly accurate potential energy functions built upon rigorous representations of the many-body expansion (MBE) of the interaction energies. This Account provides a critical overview of the performance of the MB-pol many-body potential energy function through a systematic analysis of energetic, structural, thermodynamic, and dynamical properties as well as of vibrational spectra of water from the gas to the condensed phase. It is shown that MB-pol achieves unprecedented accuracy across all phases of water through a quantitative description of each individual term of the MBE, with a physically correct representation of both short- and long-range many-body contributions. Comparisons with experimental data probing different regions of the water potential energy surface from clusters to bulk demonstrate that MB-pol represents a major step toward the long-sought-after "universal model" capable of accurately describing the molecular properties of water under different conditions and in different environments. Along this path, future challenges include the extension of the many-body scheme adopted by MB-pol to the description of generic solutes as well as the integration of MB-pol in an efficient theoretical and computational framework to model acid-base reactions in aqueous environments. In this context, given the nontraditional form of the MB-pol energy and force expressions, synergistic efforts by theoretical/computational chemists/physicists and computer scientists will be critical for the development of high-performance software for many-body molecular dynamics simulations.