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Article: A combined first-principles calculation and neural networks correction approach for evaluating Gibbs energy of formation

TitleA combined first-principles calculation and neural networks correction approach for evaluating Gibbs energy of formation
Authors
KeywordsDFT
First-principles quantum mechanical methods
Gibbs energy of formation
Neural network
Issue Date2004
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/08927022.asp
Citation
Molecular Simulation, 2004, v. 30 n. 1, p. 9-15 How to Cite?
AbstractDespite of their successes, the results of first-principles quantum mechanical calculations contain inherent numerical errors that are caused by inadequate treatment of electron correlation, incompleteness of basis sets, relativistic effects or approximated exchange-correlation functionals. In this work, we develop a combined density-functional theory and neural-network correction (DFT-NEURON) approach to reduce drastically these errors, and apply the resulting approach to determine the standard Gibbs energy of formation ΔG° at 298 K for small- and medium-sized organic molecules. The root mean square deviation of the calculated ΔG° for 180 molecules is reduced from 22.3kcal-mol-1 to 3.0 kcal-mol-1 for B3LYP/6-311 + G(d,p). We examine further the selection of physical descriptors for the neural network.
Persistent Identifierhttp://hdl.handle.net/10722/69312
ISSN
2015 Impact Factor: 1.678
2015 SCImago Journal Rankings: 0.535
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Xen_HK
dc.contributor.authorHu, Len_HK
dc.contributor.authorWong, Len_HK
dc.contributor.authorChen, Gen_HK
dc.date.accessioned2010-09-06T06:12:30Z-
dc.date.available2010-09-06T06:12:30Z-
dc.date.issued2004en_HK
dc.identifier.citationMolecular Simulation, 2004, v. 30 n. 1, p. 9-15en_HK
dc.identifier.issn0892-7022en_HK
dc.identifier.urihttp://hdl.handle.net/10722/69312-
dc.description.abstractDespite of their successes, the results of first-principles quantum mechanical calculations contain inherent numerical errors that are caused by inadequate treatment of electron correlation, incompleteness of basis sets, relativistic effects or approximated exchange-correlation functionals. In this work, we develop a combined density-functional theory and neural-network correction (DFT-NEURON) approach to reduce drastically these errors, and apply the resulting approach to determine the standard Gibbs energy of formation ΔG° at 298 K for small- and medium-sized organic molecules. The root mean square deviation of the calculated ΔG° for 180 molecules is reduced from 22.3kcal-mol-1 to 3.0 kcal-mol-1 for B3LYP/6-311 + G(d,p). We examine further the selection of physical descriptors for the neural network.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/08927022.aspen_HK
dc.relation.ispartofMolecular Simulationen_HK
dc.subjectDFTen_HK
dc.subjectFirst-principles quantum mechanical methodsen_HK
dc.subjectGibbs energy of formationen_HK
dc.subjectNeural networken_HK
dc.titleA combined first-principles calculation and neural networks correction approach for evaluating Gibbs energy of formationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0892-7022&volume=30&spage=9&epage=15&date=2004&atitle=A+combined+first-principles+calculation+and+neural+networks+correction+approach+for+evaluating+gibbs+energy+of+formationen_HK
dc.identifier.emailChen, G:ghc@yangtze.hku.hken_HK
dc.identifier.authorityChen, G=rp00671en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/08927020310001631098en_HK
dc.identifier.scopuseid_2-s2.0-2342496723en_HK
dc.identifier.hkuros92533en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-2342496723&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume30en_HK
dc.identifier.issue1en_HK
dc.identifier.spage9en_HK
dc.identifier.epage15en_HK
dc.identifier.isiWOS:000187031200002-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWang, X=7501873918en_HK
dc.identifier.scopusauthoridHu, L=7401557295en_HK
dc.identifier.scopusauthoridWong, L=7402092204en_HK
dc.identifier.scopusauthoridChen, G=35253368600en_HK

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