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Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing

In this paper, we present a method to quantify the extent of disorder in a system by using conditional entropies. Our approach is especially useful when other global, or mean field, measures of disorder fail. The method is equally suited for both continuum and lattice models, and it can be made rigo...

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Autores principales: Brandani, Giovanni B., Schor, Marieke, MacPhee, Cait E., Grubmüller, Helmut, Zachariae, Ulrich, Marenduzzo, Davide
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3677935/
https://www.ncbi.nlm.nih.gov/pubmed/23762401
http://dx.doi.org/10.1371/journal.pone.0065617
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author Brandani, Giovanni B.
Schor, Marieke
MacPhee, Cait E.
Grubmüller, Helmut
Zachariae, Ulrich
Marenduzzo, Davide
author_facet Brandani, Giovanni B.
Schor, Marieke
MacPhee, Cait E.
Grubmüller, Helmut
Zachariae, Ulrich
Marenduzzo, Davide
author_sort Brandani, Giovanni B.
collection PubMed
description In this paper, we present a method to quantify the extent of disorder in a system by using conditional entropies. Our approach is especially useful when other global, or mean field, measures of disorder fail. The method is equally suited for both continuum and lattice models, and it can be made rigorous for the latter. We apply it to mixing and demixing in multicomponent fluid membranes, and show that it has advantages over previous measures based on Shannon entropies, such as a much diminished dependence on binning and the ability to capture local correlations. Further potential applications are very diverse, and could include the study of local and global order in fluid mixtures, liquid crystals, magnetic materials, and particularly biomolecular systems.
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spelling pubmed-36779352013-06-12 Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing Brandani, Giovanni B. Schor, Marieke MacPhee, Cait E. Grubmüller, Helmut Zachariae, Ulrich Marenduzzo, Davide PLoS One Research Article In this paper, we present a method to quantify the extent of disorder in a system by using conditional entropies. Our approach is especially useful when other global, or mean field, measures of disorder fail. The method is equally suited for both continuum and lattice models, and it can be made rigorous for the latter. We apply it to mixing and demixing in multicomponent fluid membranes, and show that it has advantages over previous measures based on Shannon entropies, such as a much diminished dependence on binning and the ability to capture local correlations. Further potential applications are very diverse, and could include the study of local and global order in fluid mixtures, liquid crystals, magnetic materials, and particularly biomolecular systems. Public Library of Science 2013-06-10 /pmc/articles/PMC3677935/ /pubmed/23762401 http://dx.doi.org/10.1371/journal.pone.0065617 Text en © 2013 Brandani et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Brandani, Giovanni B.
Schor, Marieke
MacPhee, Cait E.
Grubmüller, Helmut
Zachariae, Ulrich
Marenduzzo, Davide
Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing
title Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing
title_full Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing
title_fullStr Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing
title_full_unstemmed Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing
title_short Quantifying Disorder through Conditional Entropy: An Application to Fluid Mixing
title_sort quantifying disorder through conditional entropy: an application to fluid mixing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3677935/
https://www.ncbi.nlm.nih.gov/pubmed/23762401
http://dx.doi.org/10.1371/journal.pone.0065617
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