An Efficient Method of Reweighting and Reconstructing Monte Carlo Molecular Simulation Data for Extrapolation to Different Temperature and Density Conditions

Handle URI:
http://hdl.handle.net/10754/552432
Title:
An Efficient Method of Reweighting and Reconstructing Monte Carlo Molecular Simulation Data for Extrapolation to Different Temperature and Density Conditions
Authors:
Sun, Shuyu ( 0000-0002-3078-864X ) ; Kadoura, Ahmad Salim ( 0000-0001-9317-682X ) ; Salama, Amgad ( 0000-0002-4463-1010 )
Abstract:
This paper introduces an efficient technique to generate new molecular simulation Markov chains for different temperature and density conditions, which allow for rapid extrapolation of canonical ensemble averages at a range of temperatures and densities different from the original conditions where a single simulation is conducted. Obtained information from the original simulation are reweighted and even reconstructed in order to extrapolate our knowledge to the new conditions. Our technique allows not only the extrapolation to a new temperature or density, but also the double extrapolation to both new temperature and density. The method was implemented for Lennard-Jones fluid with structureless particles in single-gas phase region. Extrapolation behaviors as functions of extrapolation ranges were studied. Limits of extrapolation ranges showed a remarkable capability especially along isochors where only reweighting is required. Various factors that could affect the limits of extrapolation ranges were investigated and compared. In particular, these limits were shown to be sensitive to the number of particles used and starting point where the simulation was originally conducted.
KAUST Department:
Computational Transport Phenomena Lab; Physical Sciences and Engineering (PSE) Division
Citation:
An Efficient Method of Reweighting and Reconstructing Monte Carlo Molecular Simulation Data for Extrapolation to Different Temperature and Density Conditions 2013, 18:2147 Procedia Computer Science
Publisher:
Elsevier BV
Journal:
Procedia Computer Science
Conference/Event name:
13th Annual International Conference on Computational Science, ICCS 2013
Issue Date:
1-Jun-2013
DOI:
10.1016/j.procs.2013.05.385
Type:
Conference Paper
ISSN:
18770509
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1877050913005280
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division; Computational Transport Phenomena Lab

Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Shuyuen
dc.contributor.authorKadoura, Ahmad Salimen
dc.contributor.authorSalama, Amgaden
dc.date.accessioned2015-05-07T13:54:01Zen
dc.date.available2015-05-07T13:54:01Zen
dc.date.issued2013-06-01en
dc.identifier.citationAn Efficient Method of Reweighting and Reconstructing Monte Carlo Molecular Simulation Data for Extrapolation to Different Temperature and Density Conditions 2013, 18:2147 Procedia Computer Scienceen
dc.identifier.issn18770509en
dc.identifier.doi10.1016/j.procs.2013.05.385en
dc.identifier.urihttp://hdl.handle.net/10754/552432en
dc.description.abstractThis paper introduces an efficient technique to generate new molecular simulation Markov chains for different temperature and density conditions, which allow for rapid extrapolation of canonical ensemble averages at a range of temperatures and densities different from the original conditions where a single simulation is conducted. Obtained information from the original simulation are reweighted and even reconstructed in order to extrapolate our knowledge to the new conditions. Our technique allows not only the extrapolation to a new temperature or density, but also the double extrapolation to both new temperature and density. The method was implemented for Lennard-Jones fluid with structureless particles in single-gas phase region. Extrapolation behaviors as functions of extrapolation ranges were studied. Limits of extrapolation ranges showed a remarkable capability especially along isochors where only reweighting is required. Various factors that could affect the limits of extrapolation ranges were investigated and compared. In particular, these limits were shown to be sensitive to the number of particles used and starting point where the simulation was originally conducted.en
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1877050913005280en
dc.rightsArchived with thanks to Procedia Computer Science. http://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectMolecular simulationen
dc.subjectMonte Carlo simulationen
dc.subjectBoltzmann distributionen
dc.subjectMC chain reweightingen
dc.subjectMC chain reconstructionen
dc.titleAn Efficient Method of Reweighting and Reconstructing Monte Carlo Molecular Simulation Data for Extrapolation to Different Temperature and Density Conditionsen
dc.typeConference Paperen
dc.contributor.departmentComputational Transport Phenomena Laben
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalProcedia Computer Scienceen
dc.conference.date2013-06-05 to 2013-06-07en
dc.conference.name13th Annual International Conference on Computational Science, ICCS 2013en
dc.conference.locationBarcelona, ESPen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorSun, Shuyuen
kaust.authorKadoura, Ahmad Salimen
kaust.authorSalama, Amgaden
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