Generalized Regression Neural Network Based Quantitative Structure-Property Relationship for the Prediction of Absorption Energy

DOI

10.2991/citcs.2012.123How to use a DOI?

Keywords

Generalized Regression Neural Network; Sample subset partitioning; Kennard and Stones algorithm; Absorption energy; Density functional theory

Abstract

Generalized Regression Neural Network (GRNN) was used to develop a quantitative structure-property relationship (QSPR) model to improve the calculation accuracy of density functional theory (DFT). The model has been applied to evaluate optical absorption energies of 150 organic molecules based on the molecular descriptors. The entire dataset was divided into a training set of 120 molecules and a test set of 30 molecules according to the method, termed SPXY (Sample set Partitioning based on joint x?y distances), extended Kennard and Stones (KS) algorithm according to their differences in both x (instrumental responses) and y (predicted parameter) spaces in the calculation of inter-sample distances. Back-propagation neural network with SPXY partitioning algorithm (BPNN-SPXY) and GRNN with KS algorithm (GRNN-KS) were also utilized to construct model to compare with the results obtained by GRNN with SPXY algorithm (GRNN-SPXY). The root-mean-square errors in absorption energy predictions for the whole data set given by DFT, BPNN-SPXY , GRNN-KS and GRNN-SPXY were 0.47, 0.21, 0.17 and 0.13, respectively. The GRNN-SPXY prediction results are in good agreement with the experimental value of absorption energy.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hui Li
AU  - Yinghua Lu
AU  - Ting Gao
AU  - Hongzhi Li
AU  - Lihong Hu
PY  - 2012/11
DA  - 2012/11
TI  - Generalized Regression Neural Network Based Quantitative Structure-Property Relationship for the Prediction of Absorption Energy
BT  - Proceedings of the 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 476
EP  - 479
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.123
DO  - 10.2991/citcs.2012.123
ID  - Li2012/11
ER  -