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Article: Improving the accuracy of density-functional theory calculation: The statistical correction approach

TitleImproving the accuracy of density-functional theory calculation: The statistical correction approach
Authors
Issue Date2004
PublisherAmerican Chemical Society. The Journal's web site is located at http://pubs.acs.org/jpca
Citation
Journal Of Physical Chemistry A, 2004, v. 108 n. 40, p. 8514-8525 How to Cite?
AbstractRecently, a novel, neural-networks-based method, the DFT-NEURON method, was developed to improve the accuracy of first-principles calculations and was applied to correct the systematic deviations of the calculated heats of formation for small-to-medium-sized organic molecules (Hu, L. H.; Wang, X. J.; Wong, L. H.; Chen, G. H. J. Chem. Phys. 2003, 119, 11501). In this work, we examine its theoretical foundation and generalize it to adopt any other statistical correction approaches, in particular, the multiple linear regression method. Both neural-networks-based and multiple-linear-regression-based correction approaches are applied to calculate the Gibbs energies of formation, ionization energies, electron affinities, and absorption energies of small-to-medium-sized molecules and lead to greatly improved calculation results as compared to the conventional first-principles methods. For instance, after the neural networks correction (multiple linear regression correction), the root-mean-square (RMS) deviations of the calculated standard Gibbs energy of formation for 180 organic molecules are reduced from 12.5, 13.8, and 22.3 kcal · mol-1 to 4.7 (5.4), 3.2 (3.5), and 3.0 (3.2) kcal · mol-1 for B3LYP/6-31G(d), B3LYP/6-311+G(3df,2p), and B3LYP/6-311+G(d,p) calculations, respectively, and the RMS deviation of the calculated absorption energies of 60 organic molecules is reduced from 0.33 eV to 0.09 (0.14) eV for the TDDFT/B3LYP/6-31G(d) calculation. In general, the neural networks correction approach leads to better results than the multiple linear regression correction approach. All these demonstrate that the statistical-correction-based first-principles calculations yield excellent results and may be employed routinely as predictive tools in materials research and design.
Persistent Identifierhttp://hdl.handle.net/10722/70237
ISSN
2015 Impact Factor: 2.883
2015 SCImago Journal Rankings: 1.231
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Xen_HK
dc.contributor.authorWong, Len_HK
dc.contributor.authorHu, Len_HK
dc.contributor.authorChan, Cen_HK
dc.contributor.authorSu, Zen_HK
dc.contributor.authorChen, Gen_HK
dc.date.accessioned2010-09-06T06:21:00Z-
dc.date.available2010-09-06T06:21:00Z-
dc.date.issued2004en_HK
dc.identifier.citationJournal Of Physical Chemistry A, 2004, v. 108 n. 40, p. 8514-8525en_HK
dc.identifier.issn1089-5639en_HK
dc.identifier.urihttp://hdl.handle.net/10722/70237-
dc.description.abstractRecently, a novel, neural-networks-based method, the DFT-NEURON method, was developed to improve the accuracy of first-principles calculations and was applied to correct the systematic deviations of the calculated heats of formation for small-to-medium-sized organic molecules (Hu, L. H.; Wang, X. J.; Wong, L. H.; Chen, G. H. J. Chem. Phys. 2003, 119, 11501). In this work, we examine its theoretical foundation and generalize it to adopt any other statistical correction approaches, in particular, the multiple linear regression method. Both neural-networks-based and multiple-linear-regression-based correction approaches are applied to calculate the Gibbs energies of formation, ionization energies, electron affinities, and absorption energies of small-to-medium-sized molecules and lead to greatly improved calculation results as compared to the conventional first-principles methods. For instance, after the neural networks correction (multiple linear regression correction), the root-mean-square (RMS) deviations of the calculated standard Gibbs energy of formation for 180 organic molecules are reduced from 12.5, 13.8, and 22.3 kcal · mol-1 to 4.7 (5.4), 3.2 (3.5), and 3.0 (3.2) kcal · mol-1 for B3LYP/6-31G(d), B3LYP/6-311+G(3df,2p), and B3LYP/6-311+G(d,p) calculations, respectively, and the RMS deviation of the calculated absorption energies of 60 organic molecules is reduced from 0.33 eV to 0.09 (0.14) eV for the TDDFT/B3LYP/6-31G(d) calculation. In general, the neural networks correction approach leads to better results than the multiple linear regression correction approach. All these demonstrate that the statistical-correction-based first-principles calculations yield excellent results and may be employed routinely as predictive tools in materials research and design.en_HK
dc.languageengen_HK
dc.publisherAmerican Chemical Society. The Journal's web site is located at http://pubs.acs.org/jpcaen_HK
dc.relation.ispartofJournal of Physical Chemistry Aen_HK
dc.titleImproving the accuracy of density-functional theory calculation: The statistical correction approachen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1089-5639&volume=108&issue=40&spage=8514&epage=8525&date=2004&atitle=Improving+the+accuracy+of+density-functional+theory+calculation:+the+statistical+correction+approachen_HK
dc.identifier.emailChen, G:ghc@yangtze.hku.hken_HK
dc.identifier.authorityChen, G=rp00671en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1021/jp047263qen_HK
dc.identifier.scopuseid_2-s2.0-6344230083en_HK
dc.identifier.hkuros98478en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-6344230083&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume108en_HK
dc.identifier.issue40en_HK
dc.identifier.spage8514en_HK
dc.identifier.epage8525en_HK
dc.identifier.isiWOS:000224214100048-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridWang, X=7501873918en_HK
dc.identifier.scopusauthoridWong, L=7402092204en_HK
dc.identifier.scopusauthoridHu, L=7401557295en_HK
dc.identifier.scopusauthoridChan, C=36984615600en_HK
dc.identifier.scopusauthoridSu, Z=7402248791en_HK
dc.identifier.scopusauthoridChen, G=35253368600en_HK

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