17565499

Source:http://linkedlifedata.com/resource/pubmed/id/17565499

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0035647, umls-concept:C0205245, umls-concept:C0376315, umls-concept:C1517503, umls-concept:C2348042, umls-concept:C2698651
pubmed:issue	15
pubmed:dateCreated	2007-10-1
pubmed:abstractText	Molecular dynamics and other molecular simulation methods rely on a potential energy function, based only on the relative coordinates of the atomic nuclei. Such a function, called a force field, approximately represents the electronic structure interactions of a condensed matter system. Developing such approximate functions and fitting their parameters remains an arduous, time-consuming process, relying on expert physical intuition. To address this problem, a functional programming methodology was developed that may enable automated discovery of entirely new force-field functional forms, while simultaneously fitting parameter values. The method uses a combination of genetic programming, Metropolis Monte Carlo importance sampling and parallel tempering, to efficiently search a large space of candidate functional forms and parameters. The methodology was tested using a nontrivial problem with a well-defined globally optimal solution: a small set of atomic configurations was generated and the energy of each configuration was calculated using the Lennard-Jones pair potential. Starting with a population of random functions, our fully automated, massively parallel implementation of the method reproducibly discovered the original Lennard-Jones pair potential by searching for several hours on 100 processors, sampling only a minuscule portion of the total search space. This result indicates that, with further improvement, the method may be suitable for unsupervised development of more accurate force fields with completely new functional forms.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9878362
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Nov
pubmed:issn	0192-8651
pubmed:author	pubmed-author:PetersM DMD, pubmed-author:SlepoyAA, pubmed-author:ThompsonA PAP
pubmed:copyrightInfo	Copyright (c) 2007 Wiley Periodicals, Inc.
pubmed:issnType	Print
pubmed:day	30
pubmed:volume	28
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	2465-71
pubmed:meshHeading	pubmed-meshheading:17565499-Algorithms, pubmed-meshheading:17565499-Computer Simulation, pubmed-meshheading:17565499-Models, Genetic, pubmed-meshheading:17565499-Monte Carlo Method, pubmed-meshheading:17565499-Software Design
pubmed:year	2007
pubmed:articleTitle	Searching for globally optimal functional forms for interatomic potentials using genetic programming with parallel tempering.
pubmed:affiliation	Multiscale Dynamic Materials Modeling Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA.
pubmed:publicationType	Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't