Abstract
We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond dissociation energies (BDE) of Y-NO bond. As a demonstration, this combined quantum chemistry calculation with the GP-GRNN approach has been applied to evaluate the homolysis BDE of 92 Y-NO organic molecules. The results show that the ull-descriptor GRNN without GRA and PCA (F-GRNN) and with GRA (G-GRNN) approaches reduce the root-mean-square (RMS) of the calculated homolysis BDE of 92 organic molecules from 5.31 to 0.49 and 0.39 kcal mol−1 for the B3LYP/6-31G (d) calculation. Then the newly developed GP-GRNN approach further reduces the RMS to 0.31 kcal mol−1. Thus, the GP-GRNN correction on top of B3LYP/6-31G (d) can improve the accuracy of calculating the homolysis BDE in quantum chemistry and can predict homolysis BDE which cannot be obtained experimentally.
Highlights
Nitric oxide (NO) is an important signaling and effector molecule that is key to many physiological functions of the body, and plays a vital role in the regulation of [1,2,3,4,5,6,7,8,9]
The Generalized Regression Neural Network (GRNN) based on the grey relational analysis (GRA) and principal component analysis (PCA) (GP-generalized regression neural network (GRNN)) approach is proposed to improve the accuracy of calculating the homolysis bond dissociation energies (BDE) of 92 organic molecules
For the 92 organic molecules containing the Y-NO (Y = C, N, O, S) bond, the B3LYP function is used to optimize the geometry at 6-31G (d) basis set level and the frequency is calculated to confirm the stable structure
Summary
Nitric oxide (NO) is an important signaling and effector molecule that is key to many physiological functions of the body (e.g., blood pressure regulation, immune system and nerve conduction), and plays a vital role in the regulation of [1,2,3,4,5,6,7,8,9]. Chen and co-workers proposed a DFT-NEURON approach to establish the quantitative relationship between the experimental data and the results computed from the first main principle [23] This relationship was used to reduce the error margin of the values of the computed absorption energy [32]. Our research group proposed a successful improvement approach based on genetic algorithm and neural network (GANN) to correct the absorption energies of 150 organic molecules [33]. The Generalized Regression Neural Network (GRNN) based on the grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) approach is proposed to improve the accuracy of calculating the homolysis BDE of 92 organic molecules. The DFT B3LYP/6-31G (d) approach is first applied to optimize the carrier molecules and calculate their frequency in order to obtain the homolysis BDE value and relevant molecular descriptors of the Y-NO (Y = C, N, O, S). GP-GRNN is a more accurate and informative correction technique in chemical physics
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