Generalized Regression Neural Network Based Quantitative Structure-Property Relationship for the Prediction of Absorption Energy
Hui Li, Yinghua Lu, Ting Gao, Hongzhi Li, Lihong Hu
Available Online November 2012.
- https://doi.org/10.2991/citcs.2012.123How to use a DOI?
- Generalized Regression Neural Network; Sample subset partitioning; Kennard and Stones algorithm; Absorption energy; Density functional theory
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/citcs.2012.123 ID - Li2012/11 ER -