DocumentCode :
534917
Title :
Accurate prediction of heats of formation for c1-c16 alkanes: The genetic algorithm and neural network approach with simple input descriptors
Author :
Gao, Ting ; Li, Hong-Zhi ; Lu, Ying-Hua
Author_Institution :
Sch. of Comput. Sci. & Inf. Technol., Northeast Normal Univ., Jilin, China
Volume :
1
fYear :
2010
fDate :
13-14 Sept. 2010
Firstpage :
273
Lastpage :
276
Abstract :
Recently,the combination of genetic algorithm and neural network approach(GANN) has been carried out to improve the calculation accuracy of density functional theory. In the present work, the GANN approach with three simple input descriptors is applied to improve the accuracy of B3LYP calculation for C1-C16 alkanes. The prediction result shows that GANN is a more effective and economical techniques. The mean absolute deviations of the heats of formation of C1-C16 alkanes are 13.92, 1.05 and 0.20 kal/mol for the B3LYP, G3 and GANN methods, respectively.
Keywords :
chemical engineering computing; density functional theory; genetic algorithms; heat of formation; neural nets; organic compounds; B3LYP calculation; C1-Cι6 alkane; GANN approach; density functional theory; economical technique; genetic algorithm; heat of formation; neural network approach; simple input descriptor; Artificial neural networks; Heating;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7705-0
Type :
conf
DOI :
10.1109/CINC.2010.5643842
Filename :
5643842
Link To Document :
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