Markov state model of the two-state behaviour of water

The Dependence of Hydrophobic Interactions on the Shape of Solute Surface

According to our recent studies on hydrophobicity, this work is aimed at understanding the dependence of hydrophobic interactions on the shape of a solute's surface. It has been observed that dissolved solutes primarily affect the structure of interfacial water, which refers to the top layer of water at the interface between the solute and water. As solutes aggregate in a solution, hydrophobic interactions become closely related to the transition of water molecules from the interfacial region to the bulk water. It is inferred that hydrophobic interactions may depend on the shape of the solute surface. To enhance the strength of hydrophobic interactions, the solutes tend to aggregate, thereby minimizing their surface area-to-volume ratio. This also suggests that hydrophobic interactions may exhibit directional characteristics. Moreover, this phenomenon can be supported by calculated potential mean forces (PMFs) using molecular dynamics (MD) simulations, where different surfaces, such as convex, flat, or concave, are associated with a sphere. Furthermore, this concept can be extended to comprehend the molecular packing parameter, commonly utilized in studying the self-assembly behavior of amphiphilic molecules in aqueous solutions.

Unraveling thermodynamic anomalies of water: A molecular simulation approach to probe the two-state theory with atomistic and coarse-grained water models

Thermodynamic and dynamic anomalies of water play a crucial role in supporting life on our planet. The two-state theory attributes these anomalies to a dynamic equilibrium between locally favored tetrahedral structures (LFTSs) and disordered normal liquid structures. This theory provides a straightforward, phenomenological explanation for water's unique thermodynamic and dynamic characteristics. To validate this two-state feature, it is critical to unequivocally identify these structural motifs in a dynamically fluctuating disordered liquid. In this study, we employ a recently introduced structural parameter (θavg) that characterizes the local angular order within the first coordination shell to identify these LFTSs through molecular dynamics simulations. We employ both realistic water models with a liquid-liquid critical point (LLCP) and a coarse-grained water model without an LLCP to study water's anomalies in low-pressure regions below 2 kbar. The two-state theory consistently describes water's thermodynamic anomalies in these models, both with and without an LLCP. This suggests that the anomalies predominantly result from the two-state features rather than criticality, particularly within experimentally accessible temperature-pressure regions.

Beyond Local Structures in Critical Supercooled Water through Unsupervised Learning

The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often requires designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the liquid-liquid critical point (LLCP). Encoding the information on the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy nonlocal descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality.

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

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

Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water

Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context involves encoding the information on chemical environments using local atomic descriptors. In this work, we focus on the Smooth Overlap of Atomic Positions (SOAP) and their application in studying the properties of liquid water both in the bulk and at the hydrophobic air-water interface. By using a statistical test aimed at assessing the relative information content of different distance measures defined on the same data space, we investigate if these descriptors provide the same information as some of the common order parameters that are used to characterize local water structure such as hydrogen bonding, density, or tetrahedrality to name a few. Our analysis suggests that the ML description and the standard order parameters of the local water structure are not equivalent. In particular, a combination of these order parameters probing local water environments can predict SOAP similarity only approximately, and vice versa, the environments that are similar according to SOAP are not necessarily similar according to the standard order parameters. We also elucidate the role of some of the metaparameters in the SOAP definition in encoding chemical information.

Manifestations of the structural origin of supercooled water’s anomalies in the heterogeneous relaxation on the potential energy landscape

Liquid water is well-known for its intriguing thermodynamic anomalies in the supercooled state. The phenomenological two-state models—based on the assumption of the existence of two types of competing local states (or, structures) in liquid water—have been extremely successful in describing water’s thermodynamic anomalies. However, the precise structural features of these competing local states in liquid water still remain elusive. Here, we have employed a predefined structural order parameter-free approach to unambiguously identify two types of competing local states—entropically and energetically favored—with significantly different structural and energetic features in the TIP4P/2005 liquid water. This identification is based on the heterogeneous structural relaxation of the system in the potential energy landscape (PEL) during the steepest-descent energy minimization. This heterogeneous relaxation is characterized using order parameters inspired by the spin-glass transition in frustrated magnetic systems. We have further established a direct relationship between the population fluctuation of the two states and the anomalous behavior of the heat capacity in supercooled water. The composition-dependent spatial distribution of the entropically favored local states shows an interesting crossover from a spanning network-like single cluster to the spatially delocalized clusters in the close vicinity of the Widom line. Additionally, this study establishes a direct relationship between the topographic features of the PEL and the water’s thermodynamic anomalies in the supercooled state and provides alternate markers (in addition to the locus of maxima of thermodynamic response functions) for the Widom line in the phase plane.

Many-Body Neural Network-Based Force Field for Structure-Based Coarse-Graining of Water

High-fidelity results from atomistic simulations can only be obtained by using accurate force-field (FF) parameters. Although empirical FFs are commonly used in the modeling of atomistic systems due to their simplicity, they have many limitations inherent in the crude approximations associated with their analytical form. Recent advances in neural network-based FFs have led to more accurate FFs by using symmetry functions or full many-body expansions. However, this approach leads to several issues including the arbitrariness of the symmetry functions, and the intangible and uninterpretable interactions which are only known once the positions of all atoms are set. More importantly, training is another bottleneck, as high-quality force and energy information is required, which is usually not accessible from experimental data. To solve these issues within the context of structure-based coarse-graining methods, we switch in this work to a local-search method to target the reference structure instead of using conventional backpropagation algorithms used to target the forces and energies of the reference structure. Our FF is decomposed into two-, three-, and higher-order terms, where each term is modeled with a separate neural network. To show the versatility of our method, we study four different systems, namely, Stillinger-Weber particles as an atomistic case and three water models, namely SPC/E, MB-pol, and ab initio, as coarse-graining cases. We show the successful application of our approach, by reproducing structural properties of different water models, followed by providing insight into the role of two-and three-body interactions. The results of all models indicate that the double-well isotropic pair potential, the signature of water-like behavior in an isotropic system, vanishes upon inclusion of the three-body interaction, showing dominance of the three-body interaction over the two-body interaction in water-like behavior with the single-well isotropic pair potential.

Transition pathway of hydrogen bond switching in supercooled water analyzed by the Markov state model

In this work, we examine hydrogen-bond (H-bond) switching by employing the Markov State Model (MSM). During the H-bond switching, a water hydrogen initially H-bonded with water oxygen becomes H-bonded to a different water oxygen. MSM analysis was applied to trajectories generated from molecular dynamics simulations of the TIP4P/2005 model from a room-temperature state to a supercooled state. We defined four basis states to characterize the configuration between two water molecules: H-bonded ("H"), unbound ("U"), weakly H-bonded ("w"), and alternative H-bonded ("a") states. A 16 × 16 MSM matrix was constructed, describing the transition probability between states composed of three water molecules. The mean first-passage time of the H-bond switching was estimated by calculating the total flux from the HU to UH states. It is demonstrated that the temperature dependence of the mean first-passage time is in accordance with that of the H-bond lifetime determined from the H-bond correlation function. Furthermore, the flux for the H-bond switching is decomposed into individual pathways that are characterized by different forms of H-bond configurations of trimers. The dominant pathway of the H-bond switching is found to be a direct one without passing through such intermediate states as "w" and "a," the existence of which becomes evident in supercooled water. The pathway through "w" indicates a large reorientation of the donor molecule. In contrast, the pathway through "a" utilizes the tetrahedral H-bond network, which is revealed by the further decomposition based on the H-bond number of the acceptor molecule.

The anomalies and criticality of liquid water

The origin of water's anomalies has been a matter of long-standing debate. A two-state model, dating back to Röntgen, relies on the dynamical coexistence of two types of local structures-locally favored tetrahedral structure (LFTS) and disordered normal-liquid structure (DNLS)-in liquid water. Phenomenologically, this model not only explains water's thermodynamic anomalies but also can rationalize the existence of a liquid-liquid critical point (LLCP) if there is a cooperative formation of LFTS. We recently found direct evidence for the coexistence of LFTS and DNLS in the experimental structure factor of liquid water. However, the existence of the LLCP and its impact on water's properties has remained elusive, leaving the origin of water's anomalies unclear. Here we propose a unique strategy to locate the LLCP of liquid water. First, we make a comprehensive analysis of a large set of experimental structural, thermodynamic, and dynamic data based on our hierarchical two-state model. This model predicts that the two thermodynamic and dynamical fluctuation maxima lines should cross at the LLCP if it exists, which we confirm by hundred-microsecond simulations for model waters. Based on recent experimental results of the compressibility and diffusivity measurements in the no man's land, we reveal that the two lines cross around 184 K and 173 MPa for real water, suggesting the presence of the LLCP around there. Nevertheless, we find that the criticality is almost negligible in the experimentally accessible region of liquid water because it is too far from the LLCP. Our findings would provide a clue to settle the long-standing debate.

Decoding a Percolation Phase Transition of Water at ∼330 K with a Nanoparticle Ruler

Liquid water, despite its simple molecular structure, remains one of the most fascinating and complex substances. Most notably, many questions continue to exist regarding the phase transitions and anomalous properties of water, which are subtle to observe experimentally. Here, we report a sharp transition in water at 330 K unveiled through experimental measurements of the instantaneous Brownian velocity of NaYF₄:Yb/Er upconversion nanoparticles in water. Our experimental investigations, corroborated by molecular dynamics simulations, elucidate a geometrical phase transition where a low-density liquid (LDL) clusters become percolated below 330 K. Around this critical temperature, we find the sizes of the LDL clusters to be similar to those of the nanoparticles, confirming the role of the upconversion nanoparticle as a powerful ruler for measuring the extensiveness of the LDL hydrogen-bond network and nanometer-scale spatial changes (20-100 nm) in liquids. Additionally, a new order parameter that unequivocally classifies water molecules into two local geometric states is introduced, providing a new tool for understanding and modeling water's many anomalous properties and phase transitions.

Feature vector clustering molecular pairs in computer simulations

A clustering framework is introduced to analyze the microscopic structural organization of molecular pairs in liquids and solutions. A molecular pair is represented by a representative vector (RV). To obtain RV, intermolecular atom distances in the pair are extracted from simulation trajectory as components of the key feature vector (KFV). A specific scheme is then suggested to transform KFV to RV by removing the influence of permutational molecular symmetry on the KFV as the predicted clusters should be independent of possible permutations of identical atoms in the pair. After RVs of pairs are obtained, a clustering analysis technique is finally used to classify all the RVs of molecular pairs into the clusters. The framework is applied to analyze trajectory from molecular dynamics simulations of an ionic liquid (trihexyltetradecylphosphonium bis(oxalato)borate ([P_6,6,6,14 ][BOB])). The molecular pairs are successfully categorized into physically meaningful clusters, and their effectiveness is evaluated by computing the product moment correlation coefficient (PMCC). (Willett, Winterman, and Bawden, J. Chem. Inf. Comput. Sci. 1986, 26, 109-118; Downs, Willett, and Fisanick, J. Chem. Inf. Comput. Sci. 1994, 34, 1094-1102) It is observed that representative configurations of two clusters are related to two energy local minimum structures optimized by density functional theory (DFT) calculation, respectively. Several widely used clustering analysis techniques of both nonhierarchical (k-means) and hierarchical clustering algorithms are also evaluated and compared with each other. The proposed KFV technique efficiently reveals local molecular pair structures in the simulated complex liquid. It is a method, which is highly useful for liquids and solutions in particular with strong intermolecular interactions. © 2019 Wiley Periodicals, Inc.

Decoding signatures of structure, bulk thermodynamics, and solvation in three-body angle distributions of rigid water models

A tetrahedral structure resulting from hydrogen bonding is a hallmark of liquid water and plays a significant role in determining its unique thermophysical properties. This water feature has helped understand anomalous properties and physically interpret and model hydrophobic solvation thermodynamics. Tetrahedrality is well described by the geometric relationship of any central water molecule with two of its nearest neighbors in the first coordination shell, as defined by the corresponding "three-body" angle. While order parameters and even full water models have been developed using specific or average features of the three-body angle distribution, here we examine the distribution holistically, tracking its response to changes in temperature, density, and the presence of model solutes. Surprisingly, we find that the three-body distribution responds by varying primarily along a single degree of freedom, suggesting a remarkably simplified view of water structure. We characterize three-body angle distributions across temperature and density space and identify principal components of the variations with state conditions. We show that these principal components embed physical significance and trace out transitions between tetrahedral and simple-fluid-like behavior. Moreover, we find that the ways three-body angles vary within the hydration shells of model colloids of different types and sizes are nearly identical to the variations seen in bulk water across density and temperature. Importantly, through the principal directions of these variations, we find that perturbations to the hydration-water distributions well predict the thermodynamics associated with colloid solvation, in particular, the relative entropy of this process that captures indirect, solvent-mediated contributions to the hydration free energy.

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems.

Understanding local dynamical processes in materials is challenging due to the complexity of the local atomic environments. Here the authors propose a graph dynamical networks approach that is shown to learn the atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations.

Translational and rotational dynamics of high and low density TIP4P/2005 water

We use molecular dynamics simulations using TIP4P/2005 to investigate the self- and distinct-van Hove functions for different local environments of water, classified using the local structure index as an order parameter. The orientational dynamics were studied through the calculation of the time-correlation functions of different-order Legendre polynomials in the OH-bond unit vector. We found that the translational and orientational dynamics are slower for molecules in a low-density local environment and correspondingly the mobility is enhanced upon increasing the local density, consistent with some previous works, but opposite to a recent study on the van Hove function. From the analysis of the distinct dynamics, we find that the second and fourth peaks of the radial distribution function, previously identified as low density-like arrangements, show long persistence in time. The analysis of the time-dependent interparticle distance between the central molecule and the first coordination shell shows that particle identity persists longer than distinct van Hove correlations. The motion of two first-nearest-neighbor molecules thus remains coupled even when this correlation function has been completely decayed. With respect to the orientational dynamics, we show that correlation functions of molecules in a low-density environment decay exponentially, while molecules in a local high-density environment exhibit bi-exponential decay, indicating that dynamic heterogeneity of water is associated with the heterogeneity among high-density and between high-density and low-density species. This bi-exponential behavior is associated with the existence of interstitial waters and the collapse of the second coordination sphere in high-density arrangements, but not with H-bond strength.

Spurious violation of the Stokes-Einstein-Debye relation in supercooled water

The theories of Brownian motion, the Debye rotational diffusion model, and hydrodynamics together provide us with the Stokes–Einstein–Debye (SED) relation between the rotational relaxation time of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\boldsymbol{\ell }}$$\end{document}ℓ-th degree Legendre polynomials \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\boldsymbol{\tau }}}_{{\boldsymbol{\ell }}}$$\end{document}τℓ, and viscosity divided by temperature, η/T. Experiments on supercooled liquids are frequently performed to measure the SED relations, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\boldsymbol{\tau }}}_{{\boldsymbol{\ell }}}$$\end{document}τℓk_BT/η and D_t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\boldsymbol{\tau }}}_{{\boldsymbol{\ell }}}$$\end{document}τℓ, where D_t is the translational diffusion constant. However, the SED relations break down, and its molecular origin remains elusive. Here, we assess the validity of the SED relations in TIP4P/2005 supercooled water using molecular dynamics simulations. Specifically, we demonstrate that the higher-order \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\boldsymbol{\tau }}}_{{\boldsymbol{\ell }}}$$\end{document}τℓ values exhibit a temperature dependence similar to that of η/T, whereas the lowest-order \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\boldsymbol{\tau }}}_{{\boldsymbol{\ell }}}$$\end{document}τℓ values are decoupled with η/T, but are coupled with the translational diffusion constant D_t. We reveal that the SED relations are so spurious that they significantly depend on the degree of Legendre polynomials.

High and low density patches in simulated liquid water

We present insights into the nature of structural heterogeneities in liquid water by characterizing the empty space within the hydrogen bond network. Using molecular dynamics simulations, we show that density fluctuations create regions of empty space characterized by a diverse morphology - from spherical to fractal-like voids. These voids allow for the identification of low and high density patches of the liquid, encompassing short (0.3-0.5 nm) as well as long (1-2 nm) length-scales. In addition, we show that the formation of these patches is coupled to collective fluctuations involving the topology of hydrogen-bonded rings of water molecules. In particular, water molecules in the high density patches tend to be slightly more tetrahedral - which is consistent with the predictions of the hydrophobic effect.

Solvation Layer of Antifreeze Proteins Analyzed with a Markov State Model

Three structurally very different antifreeze proteins (AFPs) are studied, addressing the question as to what extent the hypothesized preordering-binding mechanism is still relevant in the second solvation layer of the protein and beyond. Assuming a two-state model of water, the solvation layers are analyzed with the help of molecular dynamics simulations together with a Markov state model, which investigates the local tedrahedrality of the water hydrogen-bond network around a given water molecule. It has been shown previously that this analysis can discriminate the high-entropy, high-density state of the liquid (HDL) from its more structured low-density state (LDL). All investigated proteins, regardless of whether they are an AFP or not, have a tendency to increase the amount of HDL in their second solvation layer. The ice binding site (IBS) of the antifreeze proteins counteracts that trend, with either a hole in the HDL layer or a true excess of LDL. The results correlate to a certain extent with recent experiments, which have observed ice-like vibrational (VSFG) spectra for the water atop the IBS of only a subset of antifreeze proteins. It is concluded that the preordering-binding mechanism indeed seems to play a role but is only part of the overall picture.

Impact of local symmetry breaking on the physical properties of tetrahedral liquids

Water and silica are the most important materials with local tetrahedral symmetry. They have similar crystalline polymorphs and exhibit anomalous density maximum in the liquid state. However, water and silica also show very different characteristics. For instance, the density of water varies much more sharply than that of liquid silica near the maximum as temperature changes. More notably, silica is a very good glass-former, but water is an extremely poor one. The physical origins of these similarities and differences still remain elusive, due to the lack of a microscopic understanding of the structural ordering in these two important liquids. Here, by accessing microscopic structural information by computer simulations, we reveal that local translational symmetry breaking is responsible for the density anomalies. On the other hand, the difference in the degree of local orientational symmetry breaking between water and silica, which originates from the difference in their bonding nature, causes not only the difference in the sharpness of density anomalies, but also their distinct glass-forming abilities. Our work not only shows the crucial roles of local translational and orientational symmetry breaking in the physical properties of the two extremely important materials, water and silica, but also provides a unified scenario applicable for other tetrahedral liquids such as Si, Ge, C, BeF₂, and GeO₂.

Supercooled and glassy water: Metastable liquid(s), amorphous solid(s), and a no-man's land

We review the recent research on supercooled and glassy water, focusing on the possible origins of its complex behavior. We stress the central role played by the strong directionality of the water-water interaction and by the competition between local energy, local entropy, and local density. In this context we discuss the phenomenon of polyamorphism (i.e., the existence of more than one disordered solid state), emphasizing both the role of the preparation protocols and the transformation between the different disordered ices. Finally, we present the ongoing debate on the possibility of linking polyamorphism with a liquid-liquid transition that could take place in the no-man's land, the temperature-pressure window in which homogeneous nucleation prevents the investigation of water in its metastable liquid form.