Computing the viscosity of supercooled liquids: Markov Network model.

The microscopic origin of glass transition, when liquid viscosity changes continuously by more than ten orders of magnitude, is challenging to explain from first principles. Here we describe the detailed derivation and implementation of a Markovian Network model to calculate the shear viscosity of d...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ju Li, Akihiro Kushima, Jacob Eapen, Xi Lin, Xiaofeng Qian, John C Mauro, Phong Diep, Sidney Yip
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c86927a89ded47f78743e0359e397296
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c86927a89ded47f78743e0359e397296
record_format	dspace
spelling	oai:doaj.org-article:c86927a89ded47f78743e0359e3972962021-11-18T06:56:45ZComputing the viscosity of supercooled liquids: Markov Network model.1932-620310.1371/journal.pone.0017909https://doaj.org/article/c86927a89ded47f78743e0359e3972962011-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21464988/?tool=EBIhttps://doaj.org/toc/1932-6203The microscopic origin of glass transition, when liquid viscosity changes continuously by more than ten orders of magnitude, is challenging to explain from first principles. Here we describe the detailed derivation and implementation of a Markovian Network model to calculate the shear viscosity of deeply supercooled liquids based on numerical sampling of an atomistic energy landscape, which sheds some light on this transition. Shear stress relaxation is calculated from a master-equation description in which the system follows a transition-state pathway trajectory of hopping among local energy minima separated by activation barriers, which is in turn sampled by a metadynamics-based algorithm. Quantitative connection is established between the temperature variation of the calculated viscosity and the underlying potential energy and inherent stress landscape, showing a different landscape topography or "terrain" is needed for low-temperature viscosity (of order 10(7) Pa·s) from that associated with high-temperature viscosity (10(-5) Pa·s). Within this range our results clearly indicate the crossover from an essentially Arrhenius scaling behavior at high temperatures to a low-temperature behavior that is clearly super-Arrhenius (fragile) for a Kob-Andersen model of binary liquid. Experimentally the manifestation of this crossover in atomic dynamics continues to raise questions concerning its fundamental origin. In this context this work explicitly demonstrates that a temperature-dependent "terrain" characterizing different parts of the same potential energy surface is sufficient to explain the signature behavior of vitrification, at the same time the notion of a temperature-dependent effective activation barrier is quantified.Ju LiAkihiro KushimaJacob EapenXi LinXiaofeng QianJohn C MauroPhong DiepSidney YipPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 3, p e17909 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ju Li Akihiro Kushima Jacob Eapen Xi Lin Xiaofeng Qian John C Mauro Phong Diep Sidney Yip Computing the viscosity of supercooled liquids: Markov Network model.
description	The microscopic origin of glass transition, when liquid viscosity changes continuously by more than ten orders of magnitude, is challenging to explain from first principles. Here we describe the detailed derivation and implementation of a Markovian Network model to calculate the shear viscosity of deeply supercooled liquids based on numerical sampling of an atomistic energy landscape, which sheds some light on this transition. Shear stress relaxation is calculated from a master-equation description in which the system follows a transition-state pathway trajectory of hopping among local energy minima separated by activation barriers, which is in turn sampled by a metadynamics-based algorithm. Quantitative connection is established between the temperature variation of the calculated viscosity and the underlying potential energy and inherent stress landscape, showing a different landscape topography or "terrain" is needed for low-temperature viscosity (of order 10(7) Pa·s) from that associated with high-temperature viscosity (10(-5) Pa·s). Within this range our results clearly indicate the crossover from an essentially Arrhenius scaling behavior at high temperatures to a low-temperature behavior that is clearly super-Arrhenius (fragile) for a Kob-Andersen model of binary liquid. Experimentally the manifestation of this crossover in atomic dynamics continues to raise questions concerning its fundamental origin. In this context this work explicitly demonstrates that a temperature-dependent "terrain" characterizing different parts of the same potential energy surface is sufficient to explain the signature behavior of vitrification, at the same time the notion of a temperature-dependent effective activation barrier is quantified.
format	article
author	Ju Li Akihiro Kushima Jacob Eapen Xi Lin Xiaofeng Qian John C Mauro Phong Diep Sidney Yip
author_facet	Ju Li Akihiro Kushima Jacob Eapen Xi Lin Xiaofeng Qian John C Mauro Phong Diep Sidney Yip
author_sort	Ju Li
title	Computing the viscosity of supercooled liquids: Markov Network model.
title_short	Computing the viscosity of supercooled liquids: Markov Network model.
title_full	Computing the viscosity of supercooled liquids: Markov Network model.
title_fullStr	Computing the viscosity of supercooled liquids: Markov Network model.
title_full_unstemmed	Computing the viscosity of supercooled liquids: Markov Network model.
title_sort	computing the viscosity of supercooled liquids: markov network model.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/c86927a89ded47f78743e0359e397296
work_keys_str_mv	AT juli computingtheviscosityofsupercooledliquidsmarkovnetworkmodel AT akihirokushima computingtheviscosityofsupercooledliquidsmarkovnetworkmodel AT jacobeapen computingtheviscosityofsupercooledliquidsmarkovnetworkmodel AT xilin computingtheviscosityofsupercooledliquidsmarkovnetworkmodel AT xiaofengqian computingtheviscosityofsupercooledliquidsmarkovnetworkmodel AT johncmauro computingtheviscosityofsupercooledliquidsmarkovnetworkmodel AT phongdiep computingtheviscosityofsupercooledliquidsmarkovnetworkmodel AT sidneyyip computingtheviscosityofsupercooledliquidsmarkovnetworkmodel
_version_	1718424193725366272

Computing the viscosity of supercooled liquids: Markov Network model.

Ejemplares similares