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231224s2012 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.21969
|2 doi
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|a pubmed24n0709.xml
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|a DE-627
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|a eng
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|a Wang, Jia-Nan
|e verfasserin
|4 aut
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|a An effective method for accurate prediction of the first hyperpolarizability of alkalides
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|c 2012
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 12.07.2012
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|a Date Revised 10.12.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2011 Wiley Periodicals, Inc.
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|a The proper theoretical calculation method for nonlinear optical (NLO) properties is a key factor to design the excellent NLO materials. Yet it is a difficult task to obatin the accurate NLO property of large scale molecule. In present work, an effective intelligent computing method, as called extreme learning machine-neural network (ELM-NN), is proposed to predict accurately the first hyperpolarizability (β(0)) of alkalides from low-accuracy first hyperpolarizability. Compared with neural network (NN) and genetic algorithm neural network (GANN), the root-mean-square deviations of the predicted values obtained by ELM-NN, GANN, and NN with their MP2 counterpart are 0.02, 0.08, and 0.17 a.u., respectively. It suggests that the predicted values obtained by ELM-NN are more accurate than those calculated by NN and GANN methods. Another excellent point of ELM-NN is the ability to obtain the high accuracy level calculated values with less computing cost. Experimental results show that the computing time of MP2 is 2.4-4 times of the computing time of ELM-NN. Thus, the proposed method is a potentially powerful tool in computational chemistry, and it may predict β(0) of the large scale molecules, which is difficult to obtain by high-accuracy theoretical method due to dramatic increasing computational cost
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Metals, Alkali
|2 NLM
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| 700 |
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|a Xu, Hong-Liang
|e verfasserin
|4 aut
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|a Sun, Shi-Ling
|e verfasserin
|4 aut
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| 700 |
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|a Gao, Ting
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Hong-Zhi
|e verfasserin
|4 aut
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| 700 |
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|a Li, Hui
|e verfasserin
|4 aut
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| 700 |
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|a Su, Zhong-Min
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 33(2012), 2 vom: 15. Jan., Seite 231-6
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:33
|g year:2012
|g number:2
|g day:15
|g month:01
|g pages:231-6
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|u http://dx.doi.org/10.1002/jcc.21969
|3 Volltext
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