Anomalies and Local Structure of Liquid Water from Boiling to the Supercooled Regime as Predicted by the Many-Body MB-pol Model

Modeling Many-Body Interactions in Water with Gaussian Process Regression

We report a first-principles water dimer potential that captures many-body interactions through Gaussian process regression (GPR). Modeling is upgraded from previous work by using a custom kernel function implemented through the KeOps library, allowing for much larger GPR models to be constructed and interfaced with the next-generation machine learning force field FFLUX. A new synthetic water dimer data set, called WD24, is used for model training. The resulting models can predict 90% of dimer geometries within chemical accuracy for a test set and in a simulation. The curvature of the potential energy surface is captured by the models, and a successful geometry optimization is completed with a total energy error of just 2.6 kJ mol-1, from a starting structure where water molecules are separated by nearly 4.3 Å. Dimeric modeling of a flexible, noncrystalline system with FFLUX is shown for the first time.

Current Status of the MB-pol Data-Driven Many-Body Potential for Predictive Simulations of Water Across Different Phases

Developing a molecular-level understanding of the properties of water is central to numerous scientific and technological applications. However, accurately modeling water through computer simulations has been a significant challenge due to the complex nature of the hydrogen-bonding network that water molecules form under different thermodynamic conditions. This complexity has led to over five decades of research and many modeling attempts. The introduction of the MB-pol data-driven many-body potential energy function marked a significant advancement toward a universal molecular model capable of predicting the structural, thermodynamic, dynamical, and spectroscopic properties of water across all phases. By integrating physics-based and data-driven (i.e., machine-learned) components, which correctly capture the delicate balance among different many-body interactions, MB-pol achieves chemical and spectroscopic accuracy, enabling realistic molecular simulations of water, from gas-phase clusters to liquid water and ice. In this review, we present a comprehensive overview of the data-driven many-body formalism adopted by MB-pol, highlight the main results and predictions made from computer simulations with MB-pol to date, and discuss the prospects for future extensions to data-driven many-body potentials of generic and reactive molecular systems.

Five-Site Water Models for Ice and Liquid Water Generated by a Series-Parallel Machine Learning Strategy

Icing, a common natural phenomenon, always originates from a molecule. Molecular simulation is crucial for understanding the relevant process but still faces a great challenge in obtaining a uniform and accurate description of ice and liquid water with limited model parameters. Here, we propose a series-parallel machine learning (ML) approach consisting of a classification back-propagation neural network (BPNN), parallel regression BPNNs, and a genetic algorithm to establish conventional TIP5P-BG and temperature-dependent TIP5P-BGT models. The established water models exhibit a comprehensive balance among the crucial physical properties (melting point, density, vaporization enthalpy, self-diffusion coefficient, and viscosity) with mean absolute percentage errors of 2.65 and 2.40%, respectively, and excellent predictive performance on the related properties of liquid water. For ice, the simulation results on the critical nucleus size and growth rate are in good accordance with experiments. This work offers a powerful molecular model for phase transition and icing in nanoconfinement and a construction strategy for a complex molecular model in the extreme case.

Computation of Overhauser dynamic nuclear polarization processes reveals fundamental correlation between water dynamics, structure, and solvent restructuring entropy

Hydration water dynamics, structure, and thermodynamics are crucially important to understand and predict water-mediated properties at molecular interfaces. Yet experimentally and directly quantifying water behavior locally near interfaces at the sub-nanometer scale is challenging, especially at interfaces submerged in biological solutions. Overhauser dynamic nuclear polarization (ODNP) experiments measure equilibrium hydration water dynamics within 8-15 angstroms of a nitroxide spin probe on instantaneous timescales (10 picoseconds to nanoseconds), making ODNP a powerful tool for probing local water dynamics in the vicinity of the spin probe. As with other spectroscopic techniques, concurrent computational analysis is necessary to gain access to detailed molecular level information about the dynamic, structural, and thermodynamic properties of water from experimental ODNP data. We chose a model system that can systematically tune the dynamics of water, a water-glycerol mixture with compositions ranging from 0 to 0.3 mole fraction glycerol. We demonstrate the ability of molecular dynamics (MD) simulations to compute ODNP spectroscopic quantities, and show that translational, rotational, and hydrogen bonding dynamics of hydration water align strongly with spectroscopic ODNP parameters. Moreover, MD simulations show tight correlations between the dynamic properties of water that ODNP captures and the structural and thermodynamic behavior of water. Hence, experimental ODNP readouts of varying water dynamics suggest changes in local structural and thermodynamic hydration water properties.

Many-body interactions and deep neural network potentials for water

We present a detailed assessment of deep neural network potentials developed within the Deep Potential Molecular Dynamics (DeePMD) framework and trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an incomplete implementation of the "nearsightedness of electronic matter" principle, which may be common throughout machine learning potentials that do not include a proper representation of self-consistently determined long-range electric fields. These findings provide further support for the "short-blanket dilemma" faced by DeePMD-based potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous, physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing discourse on the development and application of machine learning models in simulating water systems, offering insights that could guide future improvements in the field.

Water Potential from Adaptive Force Matching for Ice and Liquid with Revised Dispersion Predicts Supercooled Liquid Anomalies in Good Agreement with Two Independent Experimental Fits

A revised version of the Water potential from Adaptive force matching for Ice and Liquid (WAIL) was developed by using the previous data set for fitting the WAIL model but with a dispersion term calculated using symmetry adapted perturbation theory (SAPT). The model has no adjustable parameters and relies solely on fitting first-principles information. The new model, named revised WAIL (rWAIL), shows improved predictions of most properties of water when compared to the previously published WAIL model. The rWAIL model also compares favorably to other first-principles-derived water models, such as MB-Pol, at only a fraction of the computational cost. The rWAIL model is used to study the properties of supercooled water. The model shows evidence of a liquid-liquid phase transition (LLPT) in the supercooled regimes with the liquid-liquid critical point (LLCP) at 203 K and 90 MPa. This estimate is in good agreement with a recent polynomial fit to the experimental density of water. Also, the fit to the surface tension of supercooled water based on the rWAIL model shows excellent agreement with the corresponding fit to the experimental data. Consistent with previously published molecular dynamics and experimental data, the surface tension of water exhibits exponential growth in the supercooled regime, which is likely a result of the emergence of a low-density liquid form of water. The simulation thus unites two separate experimental fits with one first-principles-based model, lending strong evidence of an LLPT in real water.

Transferable Water Potentials Using Equivariant Neural Networks

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor-liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm3 between 230 and 365 K), vapor-liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations.

Monitoring water harvesting in metal-organic frameworks, one water molecule at a time

Metal-organic frameworks (MOFs) have gained prominence as potential materials for atmospheric water harvesting, a vital solution for arid regions and areas experiencing severe water shortages. However, the molecular factors influencing the performance of MOFs in capturing water from the air remain elusive. Among all MOFs, Ni2X2BTDD (X = F, Cl, Br) stands out as a promising water harvester due to its ability to adsorb substantial amounts of water at low relative humidity (RH). Here, we use advanced molecular dynamics simulations carried out with the state-of-the-art MB-pol data-driven many-body potential to monitor water adsorption in the three Ni2X2BTDD variants as a function of RH. Our simulations reveal that the type of halide atom in the three Ni2X2BTDD frameworks significantly influences the corresponding molecular mechanisms of water adsorption: while water molecules form strong hydrogen bonds with the fluoride atoms in Ni2F2BTDD, they tend to form hydrogen bonds with the nitrogen atoms of the triazolate linkers in Ni2Cl2BTDD and Ni2Br2BTDD. Importantly, the large size of the bromide atoms reduces the void volume in the Ni2Br2BTDD pores, which enable water molecules to initiate an extended hydrogen-bond network at lower RH. These findings not only underscore the prospect for precisely tuning structural and chemical modifications of the frameworks to optimize their interaction with water, but also highlight the predictive power of simulations with the MB-pol data-driven many-body potential. By providing a realistic description of water under different thermodynamic conditions and environments, these simulations yield unique, molecular-level insights that can guide the design and optimization of energy-efficient water harvesting materials.

Molecular driving forces for water adsorption in MOF-808: A comparative analysis with UiO-66

Metal-organic frameworks (MOFs), with their unique porous structures and versatile functionality, have emerged as promising materials for the adsorption, separation, and storage of diverse molecular species. In this study, we investigate water adsorption in MOF-808, a prototypical MOF that shares the same secondary building unit (SBU) as UiO-66, and elucidate how differences in topology and connectivity between the two MOFs influence the adsorption mechanism. To this end, molecular dynamics simulations were performed to calculate several thermodynamic and dynamical properties of water in MOF-808 as a function of relative humidity (RH), from the initial adsorption step to full pore filling. At low RH, the μ3-OH groups of the SBUs form hydrogen bonds with the initial water molecules entering the pores, which triggers the filling of these pores before the μ3-OH groups in other pores become engaged in hydrogen bonding with water molecules. Our analyses indicate that the pores of MOF-808 become filled by water sequentially as the RH increases. A similar mechanism has been reported for water adsorption in UiO-66. Despite this similarity, our study highlights distinct thermodynamic properties and framework characteristics that influence the adsorption process differently in MOF-808 and UiO-66.

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.

Descriptors of water aggregation

We rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (rOH) and HOH bond angle (θHOH), (ii) two-body: OO distance (rOO), OHO angle (θOHO), and HOOX dihedral angle (ϕHOOX), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θOOO), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (qm, m = 2, 3, 4, 5) and radius of gyration (Rg). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H2O)n, n = 3-25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that rOH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems.

Neural Network Water Model Based on the MB-Pol Many-Body Potential

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor-liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. In order to address this limitation, we developed a "deep potential" neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor-liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

Elucidating the Competitive Adsorption of H2O and CO2 in CALF-20: New Insights for Enhanced Carbon Capture Metal-Organic Frameworks

In light of the pressing need for efficient carbon capture solutions, our study investigates the simultaneous adsorption of water (H2O) and carbon dioxide (CO2) as a function of relative humidity in CALF-20, a highly scalable and stable metal-organic framework (MOF). Advanced computer simulations reveal that due to their similar interactions with the framework, H2O and CO2 molecules compete for the same binding sites, occupying similar void regions within the CALF-20 pores. This competition results in distinct thermodynamic and dynamical behaviors of H2O and CO2 molecules, depending on whether one or both guest species are present. Notably, the presence of CO2 molecules forces the H2O molecules to form more connected hydrogen-bond networks within smaller regions, slowing water reorientation dynamics and decreasing water entropy. Conversely, the presence of water speeds up the reorientation of CO2 molecules, decreases the CO2 entropy, and increases the propensity for CO2 to be adsorbed within the framework due to stronger water-mediated interactions. Due to the competition for the same void spaces, both H2O and CO2 molecules exhibit slower diffusion when molecules of the other guest species are present. These findings offer valuable strategies and insights into enhancing the differential affinity of H2O and CO2 for MOFs specifically designed for carbon capture applications.

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.

A statistical mechanical model of supercooled water based on minimal clusters of correlated molecules

In this paper, we apply a theoretical model for fluid state thermodynamics to investigate simulated water in supercooled conditions. This model, which we recently proposed and applied to sub- and super-critical fluid water [Zanetti-Polzi et al., J. Chem. Phys. 156(4), 44506 (2022)], is based on a combination of the moment-generating functions of the enthalpy and volume fluctuations as provided by two gamma distributions and provides the free energy of the system as well as other relevant thermodynamic quantities. The application we make here provides a thermodynamic description of supercooled water fully consistent with that expected by crossing the liquid-liquid Widom line, indicating the presence of two distinct liquid states. In particular, the present model accurately reproduces the Widom line temperatures estimated with other two-state models and well describes the heat capacity anomalies. Differently from previous models, according to our description, a cluster of molecules that extends beyond the first hydration shell is necessary to discriminate between the statistical fluctuation regimes typical of the two liquid states.

Imaging temperature and thickness of thin planar liquid water jets in vacuum

We present spatially resolved measurements of the temperature of a flat liquid water microjet for varying ambient pressures, from vacuum to 100% relative humidity. The entire jet surface is probed in a single shot by a high-resolution infrared camera. Obtained 2D images are substantially influenced by the temperature of the apparatus on the opposite side of the infrared camera; a protocol to correct for the thermal background radiation is presented. In vacuum, we observe cooling rates due to water evaporation on the order of 105 K/s. For our system, this corresponds to a temperature decrease in approximately 15 K between upstream and downstream positions of the flowing leaf. Making reasonable assumptions on the absorption of the thermal background radiation in the flatjet, we can extend our analysis to infer a thickness map. For a reference system, our value for the thickness is in good agreement with the one reported from white light interferometry.

Structure and thermodynamics of water adsorption in NU-1500-Cr

Metal-organic frameworks (MOFs) are a class of materials with diverse chemical and structural properties, and have been shown to effectively adsorb various types of guest molecules. The mechanism of water adsorption in NU-1500-Cr, a high-performance atmospheric water harvesting MOF, is investigated using a combination of molecular dynamics simulations and infrared spectroscopy. Calculations of thermodynamic and dynamical properties of water as a function of relative humidity allow for following the adsorption process from the initial hydration stage to complete filling of the MOF pores. Initial hydration begins at the water molecules that saturate the open Cr³⁺ sites of the framework, which is then followed by the formation of water chains that extend along the channels connecting the hexagonal pores of the framework. Water present in these channels gradually coalesces and fills the hexagonal pores sequentially after the channels are completely hydrated. The development of hydrogen-bond networks inside the MOF pores as a function of relative humidity is characterized at the molecular level using experimental and computational infrared spectroscopy. A detailed analysis of the OH-stretch vibrational band indicates that the low-frequency tail stems from strongly polarized hydrogen-bonded water molecules, suggesting the presence of some structural disorder in the experimental samples. Strategies for designing efficient water harvesting MOFs are also proposed based on the mechanism of water adsorption in NU-1500-Cr.

Anomalous Vapor and Ice Nucleation in Water at Negative Pressures: A Classical Density Functional Theory Study

In contrast to the abundance of work on the anomalous behavior of water, the relationship between the water's thermodynamic anomalies and kinetics of phase transition from metastable water is relatively unexplored. In this work, we have employed classical density functional theory to provide a unified and coherent picture of nucleation (both vapor and ice) from metastable water at negative pressure conditions. Our results suggest a peculiar nonmonotonic temperature dependence of vapor-liquid surface tension at temperatures where vapor-liquid coexistence is metastable with respect to the ice phase. The vapor nucleation barrier on isochoric cooling also shows a nonmonotonic temperature dependence. We further report that, for low density isochores, the temperature of the minimum vapor nucleation barrier (TΔΩv/min*) does not coincide with the temperature of maximum density (TMD) where metastability is maximum. The difference between the TΔΩv/min* and the TMD, however, decreases with increasing the density of the isochore. The vapor nucleation barrier along isobars shows an interesting crossover behavior in the vicinity of the Widom line on lowering the temperature. Our results on the ice nucleation suggest an anomalous retracing behavior of the nucleation barrier along isotherms at negative pressures and theoretically validate the recent findings that the reentrant ice(Ih)-liquid coexistence line can induce a drastic change in the kinetics of ice nucleation. Thus, this study establishes a direct connection between the metastable water's thermodynamic anomalies and the (vapor and ice) nucleation kinetics. In addition, this study provides deeper insights into the origin of the isothermal compressibility maximum on isochoric cooling.

Temperature Dependence of Hydrogen Bond Networks of Liquid Water: Thermodynamic Properties and Structural Heterogeneity from Topological Descriptors

Topological analyses of hydrogen bond networks were performed based on the complex network and island statistics of liquid water at different temperatures. The influence of temperature on the liquid water structures and the topological properties of the hydrogen bond networks was investigated by Metropolis Monte Carlo simulations with the TIP4P/2005 potential model. The bilinear behavior of the second peak in the radial distribution function with the temperature was properly reproduced by these simulations. The average connectivity also displayed a bilinear behavior consistent with being a local descriptor. The semiglobal average path length (or geodesic distance) descriptor showed an unprecedented trimodal distribution, whose areas were dependent on the temperature. Considering equilibrium between these three sets of networks, standard enthalpy and entropy of equilibrium were determined for the first time, providing new insights into the structural heterogeneities of liquid water with interesting perspectives for modeling these quantitative properties of hydrogen bond networks.

A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven, many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water, but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties, but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict the properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.

Stability and Metastability of Liquid Water in a Machine-Learned Coarse-Grained Model with Short-Range Interactions

Coarse-grained water models are ∼100 times more efficient than all-atom models, enabling simulations of supercooled water and crystallization. The machine-learned monatomic model ML-BOP reproduces the experimental equation of state (EOS) and ice-liquid thermodynamics at 0.1 MPa on par with the all-atom TIP4P/2005 and TIP4P/Ice models. These all-atom models were parametrized using high-pressure experimental data and are either accurate for water's EOS (TIP4P/2005) or ice-liquid equilibrium (TIP4P/Ice). ML-BOP was parametrized from temperature-dependent ice and liquid experimental densities and melting data at 0.1 MPa; its only pressure training is from compression of TIP4P/2005 ice at 0 K. Here we investigate whether ML-BOP replicates the experimental EOS and ice-water thermodynamics along all pressures of ice I. We find that ML-BOP reproduces the temperature, enthalpy, entropy, and volume of melting of hexagonal ice up to 400 MPa and the EOS of water along the melting line with an accuracy that rivals that of both TIP4P/2005 and TIP4P/Ice. We interpret that the accuracy of ML-BOP originates from its ability to capture the shift between compact and open local structures to changes in pressure and temperature. ML-BOP reproduces the sharpening of the tetrahedral peak of the pair distribution function of water upon supercooling, and its pressure dependence. We characterize the region of metastability of liquid ML-BOP with respect to crystallization and cavitation. The accessibility of ice crystallization to simulations of ML-BOP, together with its accurate representation of the thermodynamics of water, makes it promising for investigating the interplay between anomalies, glass transition, and crystallization under conditions challenging to access through experiments.

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.

Phase diagram of the TIP4P/Ice water model by enhanced sampling simulations

We studied the phase diagram for the TIP4P/Ice water model using enhanced sampling molecular dynamics simulations. Our approach is based on the calculation of ice-liquid free energy differences from biased coexistence simulations that sample reversibly the melting and growth of layers of ice. We computed a total of 19 melting points for five different ice polymorphs which are in excellent agreement with the melting lines obtained from the integration of the Clausius-Clapeyron equation. For proton-ordered and fully proton-disordered ice phases, the results are in very good agreement with previous calculations based on thermodynamic integration. For the partially-proton-disordered ice III, we find a large increase in stability that is in line with previous observations using direct coexistence simulations for the TIP4P/2005 model. This issue highlights the robustness of the approach employed here for ice polymorphs with diverse degrees of proton disorder. Our approach is general and can be applied to the calculation of other complex phase diagrams.

Melting points of water models: Current situation

By using the direct coexistence method, we have calculated the melting points of ice I h at normal pressure for three recently proposed water models, namely, TIP3P-FB, TIP4P-FB, and TIP4P-D. We obtained T m = 216 K for TIP3P-FB, T m = 242 K for TIP4P-FB, and T m = 247 K for TIP4P-D. We revisited the melting point of TIP4P/2005 and TIP5P obtaining T m = 250 and 274 K, respectively. We summarize the current situation of the melting point of ice I h for a number of water models and conclude that no model is yet able to simultaneously reproduce the melting temperature of ice I h and the temperature of the maximum in density at room pressure. This probably points toward our both still incomplete knowledge of the potential energy surface of water and the necessity of incorporating nuclear quantum effects to describe both properties simultaneously.