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Dataset . 2023
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Data sources: Datacite
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Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Shear viscosity coefficient of acqueous glycerol from non-equilibrium Molecular Dynamics simulations

Authors: Pellegrino, Michele;

Shear viscosity coefficient of acqueous glycerol from non-equilibrium Molecular Dynamics simulations

Abstract

This dataset contains the results of non-equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using the periodic perturbation technique [1]. The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains three sets of simulations, with different perturbation force parameters ("Em*"), where configuration files necessary to reproduce molecular simulations simulations are provided. Maps of density and velocity field are in "Em*"->"Flow". A small self-contained Python script to fit the velocity fields to a periodic cosine perturbation is provided (fit-periodic.py). Alternatively, viscosity can be obtained from energy outputs by running: gmx energy -f ener.edr and selecting "1/Viscosity". Simulations are performed with Gromacs. We refer to the code documentation for further information (https://manual.gromacs.org/). References: [1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) https://doi.org/10.1063/1.1421362

Keywords

Glycerol, Shear viscosity, Water, Periodic perturbation, Linear response theory, Molecular Dynamics

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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