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