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A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies
School of Computer Science and Information Technology, Northeast Normal University, Changchun 130017, China
School of Life Sciences, Northeast Normal University, Changchun 130024, China
Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University, Changchun 130024, China
* Authors to whom correspondence should be addressed.
Received: 25 May 2012; in revised form: 19 June 2012 / Accepted: 25 June 2012 / Published: 28 June 2012
Abstract: A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol−1) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol−1 to 0.15 and 0.18 kcal·mol−1, respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol−1. This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules.
Y-NO bond; homolysis
bond dissociation energies; density functional theory; self-organizing feature mapping neural network; radial basis function neural network
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Cite This Article
MDPI and ACS Style
Li, H.Z.; Hu, L.H.; Tao, W.; Gao, T.; Li, H.; Lu, Y.H.; Su, Z.M. A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies. Int. J. Mol. Sci. 2012, 13, 8051-8070.
Li HZ, Hu LH, Tao W, Gao T, Li H, Lu YH, Su ZM. A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies. International Journal of Molecular Sciences. 2012; 13(7):8051-8070.
Li, Hong Zhi; Hu, Li Hong; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min. 2012. "A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies." Int. J. Mol. Sci. 13, no. 7: 8051-8070.