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- Publisher Website: 10.1016/j.cplett.2004.04.020
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Article: A generalized exchange-correlation functional: The Neural-Networks approach
Title | A generalized exchange-correlation functional: The Neural-Networks approach |
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Authors | |
Issue Date | 2004 |
Publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/cplett |
Citation | Chemical Physics Letters, 2004, v. 390 n. 1-3, p. 186-192 How to Cite? |
Abstract | A Neural-Networks approach is employed to improve B3LYP exchange-correlation functional by taking into account of high-order contributions. The new B3LYP functional is based on a Neural-Network whose structure and synaptic weights are determined from 116 known experimental energy data [J. Chem. Phys. 98 (1993) 5648]. It leads to better agreement between the first-principles calculations and the experimental results. The new functional is further tested by applying it to calculate 40 ionization potentials and 40 enthalpies of formation in G2-2 test set [J. Chem. Phys. 109 (1998) 42] using 6-311+G(3df,2p) basis set. The root-mean-square errors are reduced from those of conventional B3LYP calculations. © 2004 Elsevier B.V. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/69105 |
ISSN | 2023 Impact Factor: 2.8 2023 SCImago Journal Rankings: 0.502 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zheng, X | en_HK |
dc.contributor.author | Hu, L | en_HK |
dc.contributor.author | Wang, X | en_HK |
dc.contributor.author | Chen, G | en_HK |
dc.date.accessioned | 2010-09-06T06:10:37Z | - |
dc.date.available | 2010-09-06T06:10:37Z | - |
dc.date.issued | 2004 | en_HK |
dc.identifier.citation | Chemical Physics Letters, 2004, v. 390 n. 1-3, p. 186-192 | en_HK |
dc.identifier.issn | 0009-2614 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/69105 | - |
dc.description.abstract | A Neural-Networks approach is employed to improve B3LYP exchange-correlation functional by taking into account of high-order contributions. The new B3LYP functional is based on a Neural-Network whose structure and synaptic weights are determined from 116 known experimental energy data [J. Chem. Phys. 98 (1993) 5648]. It leads to better agreement between the first-principles calculations and the experimental results. The new functional is further tested by applying it to calculate 40 ionization potentials and 40 enthalpies of formation in G2-2 test set [J. Chem. Phys. 109 (1998) 42] using 6-311+G(3df,2p) basis set. The root-mean-square errors are reduced from those of conventional B3LYP calculations. © 2004 Elsevier B.V. All rights reserved. | en_HK |
dc.language | eng | en_HK |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/cplett | en_HK |
dc.relation.ispartof | Chemical Physics Letters | en_HK |
dc.rights | Chemical Physics Letters. Copyright © Elsevier BV. | en_HK |
dc.title | A generalized exchange-correlation functional: The Neural-Networks approach | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0009-2614&volume=390&spage=186&epage=192&date=2004&atitle=A+generalized+exchange-correlation+functional:+The+neural-networks+approach+ | en_HK |
dc.identifier.email | Chen, G:ghc@yangtze.hku.hk | en_HK |
dc.identifier.authority | Chen, G=rp00671 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.cplett.2004.04.020 | en_HK |
dc.identifier.scopus | eid_2-s2.0-2342576829 | en_HK |
dc.identifier.hkuros | 92466 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-2342576829&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 390 | en_HK |
dc.identifier.issue | 1-3 | en_HK |
dc.identifier.spage | 186 | en_HK |
dc.identifier.epage | 192 | en_HK |
dc.identifier.isi | WOS:000221628400035 | - |
dc.publisher.place | Netherlands | en_HK |
dc.identifier.scopusauthorid | Zheng, X=7404090981 | en_HK |
dc.identifier.scopusauthorid | Hu, L=7401557295 | en_HK |
dc.identifier.scopusauthorid | Wang, X=10341267000 | en_HK |
dc.identifier.scopusauthorid | Chen, G=35253368600 | en_HK |
dc.identifier.issnl | 0009-2614 | - |