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Estimating correlation energy of diatomic molecules and atoms with neural networks

1997; Wiley; Volume: 18; Issue: 11 Linguagem: Inglês

10.1002/(sici)1096-987x(199708)18

1096-987X

Geraldo Magela e Silva, Paulo H. Acioli, Antonio Carlos Pedroza,

Chemical Thermodynamics and Molecular Structure

Journal of Computational ChemistryVolume 18, Issue 11 p. 1407-1414 Estimating correlation energy of diatomic molecules and atoms with neural networks Geraldo Magela E Silva, Corresponding Author Geraldo Magela E Silva magela@helium.fis.unb.br Departamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilDepartamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilSearch for more papers by this authorPaulo Hora Acioli, Paulo Hora Acioli Departamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilSearch for more papers by this authorAntonio Carlos Pedroza, Antonio Carlos Pedroza Departamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilSearch for more papers by this author Geraldo Magela E Silva, Corresponding Author Geraldo Magela E Silva magela@helium.fis.unb.br Departamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilDepartamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilSearch for more papers by this authorPaulo Hora Acioli, Paulo Hora Acioli Departamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilSearch for more papers by this authorAntonio Carlos Pedroza, Antonio Carlos Pedroza Departamento de Física, Universidade de Brasília, 70 910 900 Brasília DF, BrazilSearch for more papers by this author First published: 07 December 1998 https://doi.org/10.1002/(SICI)1096-987X(199708)18:11 3.0.CO;2-PCitations: 14AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The electronic correlation energy of diatomic molecules and heavy atoms is estimated using a back propagation neural network approach. The supervised learning is accomplished using known exact results of the electronic correlation energy. The recall rate, that is, the performance of the net in recognizing the training set, is about 96%. The correctness of values given to the test set and prediction rate is at the 90% level. We generate tables for the electronic correlation energy of several diatomic molecules and all the neutral atoms up to radon (Rn). © 1997 by John Wiley & Sons, Inc. J Comput Chem 18: 1407–1414, 1997 Citing Literature Volume18, Issue11August 1997Pages 1407-1414 RelatedInformation