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Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution

[Image: see text] A technology for optimization of potential parameters from condensed-phase simulations (POP) is discussed and illustrated. It is based on direct calculations of the derivatives of macroscopic observables with respect to the potential parameters. The derivatives are used in a local...

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Autores principales: Di Pierro, Michele, Mugnai, Mauro L., Elber, Ron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2014
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4306501/
https://www.ncbi.nlm.nih.gov/pubmed/25066823
http://dx.doi.org/10.1021/jp505401m
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author Di Pierro, Michele
Mugnai, Mauro L.
Elber, Ron
author_facet Di Pierro, Michele
Mugnai, Mauro L.
Elber, Ron
author_sort Di Pierro, Michele
collection PubMed
description [Image: see text] A technology for optimization of potential parameters from condensed-phase simulations (POP) is discussed and illustrated. It is based on direct calculations of the derivatives of macroscopic observables with respect to the potential parameters. The derivatives are used in a local minimization scheme, comparing simulated and experimental data. In particular, we show that the Newton trust region protocol allows for more accurate and robust optimization. We apply the newly developed technology to study the liquid mixture of tert-butanol and water. We are able to obtain, after four iterations, the correct phase behavior and accurately predict the value of the Kirkwood Buff (KB) integrals. We further illustrate that a potential that is determined solely by KB information, or the pair correlation function, is not necessarily unique.
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spelling pubmed-43065012015-07-26 Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution Di Pierro, Michele Mugnai, Mauro L. Elber, Ron J Phys Chem B [Image: see text] A technology for optimization of potential parameters from condensed-phase simulations (POP) is discussed and illustrated. It is based on direct calculations of the derivatives of macroscopic observables with respect to the potential parameters. The derivatives are used in a local minimization scheme, comparing simulated and experimental data. In particular, we show that the Newton trust region protocol allows for more accurate and robust optimization. We apply the newly developed technology to study the liquid mixture of tert-butanol and water. We are able to obtain, after four iterations, the correct phase behavior and accurately predict the value of the Kirkwood Buff (KB) integrals. We further illustrate that a potential that is determined solely by KB information, or the pair correlation function, is not necessarily unique. American Chemical Society 2014-07-26 2015-01-22 /pmc/articles/PMC4306501/ /pubmed/25066823 http://dx.doi.org/10.1021/jp505401m Text en Copyright © 2014 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Di Pierro, Michele
Mugnai, Mauro L.
Elber, Ron
Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution
title Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution
title_full Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution
title_fullStr Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution
title_full_unstemmed Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution
title_short Optimizing Potentials for a Liquid Mixture: A New Force Field for a tert-Butanol and Water Solution
title_sort optimizing potentials for a liquid mixture: a new force field for a tert-butanol and water solution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4306501/
https://www.ncbi.nlm.nih.gov/pubmed/25066823
http://dx.doi.org/10.1021/jp505401m
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