Water Diffusion Proceeds via a Hydrogen-Bond Jump Exchange Mechanism

Gomez, Axel; Piskulich, Zeke A.; Thompson, Ward H.; Laage, Damien

doi:10.1021/acs.jpclett.2c00825

Cited by 24 publications

(35 citation statements)

References 52 publications

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“…The only way to reconcile these contradictory decays is that the rotation of the OH arm is such that it re-establish the OO contact. This is the jump rotation motion advertised in recent investigations [48,49,50], and is found to be contained in the dynamics of a classical model of water. For alcohols, it is the opposite which is found, namely that the OO decay is faster than the OH decay, denoting that the hydrogen bonding holds the hydroxyl group clusters.…”

Section: Analysis Of Dynamical Correlation Functionssupporting

confidence: 57%

“…However, it is the dynamical correlations which reveal the dierence between the similarities in dierent micro-states (snapshots) and the strong dierences on how the system evolves in time from one microstate to another. Our analysis shows that it is this dynamical evolution which explains several features observed previously in the literature, such as the jump rotation of the OH arm for water [48,49,50], or the contradictory pictures of the importance of the wa-ter structure [51,33,52,14,37,39,38,40,41]. Perhaps the water tetrahedrality has been put too much at the center of water local structure [33,12,35,36],…”

Section: Discussionmentioning

confidence: 48%

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On the role charge ordering in the dynamics of cluster formation in associated liquids

Lovrinčević

Požar

Jukić

et al. 2022

Preprint

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Liquids are archetypes of disordered systems, yet liquids of polar molecules are locally more ordered than non-polar molecules, due to the Coulomb interaction based charge ordering phenomenon. Hydrogen bonded liquids, such as water or alcohols, for example, represent a special type of polar liquids, in that they form labile clustered local structures. For water, in particular, hydrogen bonding and the related local tetrahedrality, play an important role in the various attempts to understand this liquid. However, labile structures imply dynamics, and it is not clear how it affects the understanding of this type of liquids from purely static point of view. Herein, we propose to reconsider hydrogen bonding as a charge ordering process. This perspective allows to demonstrate the insufficiencies of the analysis of the microscopic structure based solely on static pair correlation functions, and the need for dynamical correlation functions, both in real and reciprocal space. This extended analysis allows to recover several aspects of our understanding of hydrogen bonded liquids, but from a unified viewpoint. For water, it confirms the jump rotation picture found recently, and it allows to rationalize the contradicting pictures that arise when following the interpretations based on hydrogen bonding. For alcohols, it allows to understand the dynamical origin of the scattering pre-peak, which does not exist for water, despite the fact that both these liquids have very similar hydroxyl group chain clusters. The concept of charge ordering complemented by the analysis of dynamical correlation functions appear as a promising way to understand micro-heterogeneity in complex liquids and mixtures from kinetics point of view

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Section: Analysis Of Dynamical Correlation Functionssupporting

confidence: 57%

Section: Discussionmentioning

confidence: 48%

On the role charge ordering in the dynamics of cluster formation in associated liquids

Lovrinčević

Požar

Jukić

et al. 2022

Preprint

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show abstract

“…In liquid water, the dynamics span time scales from subpicosecond in-place H-bond fluctuations to picosecond H-bond lifetimes and nano/microsecond diffusion rates. 58 Indeed, water dynamics are considered "fractal" in character; each time scale, no matter how short or how long, can be associated with a corresponding ensemble motion. This is particularly important in biomolecular systems, where the time scales of biomolecules can similarly span multiple orders of magnitude in time.…”

Section: Crowding Effects On H-bond Dynamicsmentioning

confidence: 99%

Importance of Hydrogen Bonding in Crowded Environments: A Physical Chemistry Perspective

You

Baiz

2022

J. Phys. Chem. A

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Cells are heterogeneous on every length and time scale; cytosol contains thousands of proteins, lipids, nucleic acids, and small molecules, and molecular interactions within this crowded environment determine the structure, dynamics, and stability of biomolecules. For decades, the effects of crowding at the atomistic scale have been overlooked in favor of more tractable models largely based on thermodynamics. Crowding can affect the conformations and stability of biomolecules by modulating water structure and dynamics within the cell, and these effects are nonlocal and environment dependent. Thus, characterizing water’s hydrogen-bond (H-bond) networks is a critical step toward a complete microscopic crowding model. This perspective provides an overview of molecular crowding and describes recent time-resolved spectroscopy approaches investigating H-bond networks and dynamics in crowded or otherwise complex aqueous environments. Ultrafast spectroscopy combined with atomistic simulations has emerged as a powerful combination for studying H-bond structure and dynamics in heterogeneous multicomponent systems. We discuss the ongoing challenges toward developing a complete atomistic description of macromolecular crowding from an experimental as well as a theoretical perspective.

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Section: Machine-learned Approximations To the Energies Of Atomistic ...mentioning

confidence: 99%

“…Indeed, recent studies have demonstrated that proton dynamics in liquid water can be well captured if the training set contains sufficient data points corresponding to proton transfers. 16 In practice, the currently most popular software for parametrizing and utilizing MLPs in electrocatalysis are LASP 17 and DeepMD. 18,19 The power of the former is the availability of a large set of predefined MLPs, while the latter has very powerful active-learning capabilities, enabling an efficient parametrization of system-specific MLPs.…”

Section: Zhi Wei Sehmentioning

confidence: 99%