DocumentCode
2952152
Title
Combined Density Functional Theory and Ensembled Elman Network Correction Approach for Electronic Excitation Energies
Author
Li, Hui ; Gao, Ting ; Lu, Yinghua ; Li, Hongzhi ; Su, Zhongmin
Author_Institution
Sch. of Comput. Sci. & Inf. Technol., Northeast Normal Univ., Changchun, China
fYear
2011
fDate
30-31 July 2011
Firstpage
1
Lastpage
4
Abstract
An ensemble of Elman networks (EEN) is formed by bagging to enhance the performance of the individual networks. The combined density functional theory (DFT) with EEN correction approach has been applied to evaluate the electronic excitation energies of organic molecules. The EEN approach improved DFT calculation results and reduced the RMS deviations from 0.48 to 0.23 eV for the training set. For the testing set, it is reduced from 0.41 to 0.22 eV. In general, the EEN approach leads to better results and shows the good generalization ability.
Keywords
physics computing; recurrent neural nets; density functional theory; discrete Fourier transforms; electronic excitation energy; ensembled Elman network correction approach; organic molecule; Accuracy; Artificial neural networks; Bagging; Context; Discrete Fourier transforms; Testing; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Control, Automation and Systems Engineering (CASE), 2011 International Conference on
Conference_Location
Singapore
Print_ISBN
978-1-4577-0859-6
Type
conf
DOI
10.1109/ICCASE.2011.5997564
Filename
5997564
Link To Document