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Article: Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach

TitleImproving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach
Authors
Issue Date2007
PublisherAmerican Institute of Physics. The Journal's web site is located at http://jcp.aip.org/jcp/staff.jsp
Citation
Journal Of Chemical Physics, 2007, v. 126 n. 14 How to Cite?
AbstractThe combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFT/B3LYP/6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV. © 2007 American Institute of Physics.
Persistent Identifierhttp://hdl.handle.net/10722/168104
ISSN
2015 Impact Factor: 2.894
2015 SCImago Journal Rankings: 0.959
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Hen_US
dc.contributor.authorShi, Len_US
dc.contributor.authorZhang, Men_US
dc.contributor.authorSu, Zen_US
dc.contributor.authorWang, Xen_US
dc.contributor.authorHu, Len_US
dc.contributor.authorChen, Gen_US
dc.date.accessioned2012-10-08T03:15:07Z-
dc.date.available2012-10-08T03:15:07Z-
dc.date.issued2007en_US
dc.identifier.citationJournal Of Chemical Physics, 2007, v. 126 n. 14en_US
dc.identifier.issn0021-9606en_US
dc.identifier.urihttp://hdl.handle.net/10722/168104-
dc.description.abstractThe combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFT/B3LYP/6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV. © 2007 American Institute of Physics.en_US
dc.languageengen_US
dc.publisherAmerican Institute of Physics. The Journal's web site is located at http://jcp.aip.org/jcp/staff.jspen_US
dc.relation.ispartofJournal of Chemical Physicsen_US
dc.rightsJournal of Chemical Physics. Copyright © American Institute of Physics.-
dc.rightsCopyright (2007) American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in (Journal of Chemical Physics, 2007, v. 26, p. 144101) and may be found at (http://jcp.aip.org/resource/1/jcpsa6/v126/i14/p144101_s1).-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleImproving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approachen_US
dc.typeArticleen_US
dc.identifier.emailChen, G:ghc@yangtze.hku.hken_US
dc.identifier.authorityChen, G=rp00671en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1063/1.2715579en_US
dc.identifier.pmid17444695-
dc.identifier.scopuseid_2-s2.0-34247223763en_US
dc.identifier.hkuros129982-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34247223763&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume126en_US
dc.identifier.issue14en_US
dc.identifier.isiWOS:000245691200004-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLi, H=8423900800en_US
dc.identifier.scopusauthoridShi, L=36161348700en_US
dc.identifier.scopusauthoridZhang, M=36043218200en_US
dc.identifier.scopusauthoridSu, Z=7402248791en_US
dc.identifier.scopusauthoridWang, X=10341267000en_US
dc.identifier.scopusauthoridHu, L=7401557295en_US
dc.identifier.scopusauthoridChen, G=35253368600en_US

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