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Article: Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach
Title | Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach |
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Authors | |
Issue Date | 2007 |
Publisher | American 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, article no. 144101 How to Cite? |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/168104 |
ISSN | 2023 Impact Factor: 3.1 2023 SCImago Journal Rankings: 1.101 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Li, H | en_US |
dc.contributor.author | Shi, L | en_US |
dc.contributor.author | Zhang, M | en_US |
dc.contributor.author | Su, Z | en_US |
dc.contributor.author | Wang, X | en_US |
dc.contributor.author | Hu, L | en_US |
dc.contributor.author | Chen, G | en_US |
dc.date.accessioned | 2012-10-08T03:15:07Z | - |
dc.date.available | 2012-10-08T03:15:07Z | - |
dc.date.issued | 2007 | en_US |
dc.identifier.citation | Journal of Chemical Physics, 2007, v. 126 n. 14, article no. 144101 | - |
dc.identifier.issn | 0021-9606 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/168104 | - |
dc.description.abstract | The 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.language | eng | en_US |
dc.publisher | American Institute of Physics. The Journal's web site is located at http://jcp.aip.org/jcp/staff.jsp | en_US |
dc.relation.ispartof | Journal of Chemical Physics | en_US |
dc.rights | Copyright 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. 126 n. 14, article no. 144101 and may be found at https://doi.org/10.1063/1.2715579 | - |
dc.title | Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach | en_US |
dc.type | Article | en_US |
dc.identifier.email | Chen, G:ghc@yangtze.hku.hk | en_US |
dc.identifier.authority | Chen, G=rp00671 | en_US |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1063/1.2715579 | en_US |
dc.identifier.pmid | 17444695 | - |
dc.identifier.scopus | eid_2-s2.0-34247223763 | en_US |
dc.identifier.hkuros | 129982 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-34247223763&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 126 | en_US |
dc.identifier.issue | 14 | en_US |
dc.identifier.spage | article no. 144101 | - |
dc.identifier.epage | article no. 144101 | - |
dc.identifier.isi | WOS:000245691200004 | - |
dc.publisher.place | United States | en_US |
dc.identifier.scopusauthorid | Li, H=8423900800 | en_US |
dc.identifier.scopusauthorid | Shi, L=36161348700 | en_US |
dc.identifier.scopusauthorid | Zhang, M=36043218200 | en_US |
dc.identifier.scopusauthorid | Su, Z=7402248791 | en_US |
dc.identifier.scopusauthorid | Wang, X=10341267000 | en_US |
dc.identifier.scopusauthorid | Hu, L=7401557295 | en_US |
dc.identifier.scopusauthorid | Chen, G=35253368600 | en_US |
dc.identifier.issnl | 0021-9606 | - |