An Accurate and Efficient Method to Predict Y-NO Bond Homolysis Bond Dissociation Energies

The paper suggests a new method that combines the Kennard and Stone algorithm (Kenstone, KS), hierarchical clustering (HC), and ant colony optimization (ACO)-based extreme learning machine (ELM) (KS-HC/ACO-ELM) with the density functional theory (DFT) B3LYP/6-31G(d) method to improve the accuracy of DFT calculations for the Y-NO homolysis bond dissociation energies (BDE). In this method, Kenstone divides the whole data set into two parts, the training set and the test set; HC and ACO are used to perform the cluster analysis on molecular descriptors; correlation analysis is applied for selecting the most correlated molecular descriptors in the classes, and ELM is the nonlinear model for establishing the relationship between DFT calculations and homolysis BDE experimental values. The results show that the standard deviation of homolysis BDE in the molecular test set is reduced from 4.03 kcal mol −1 calculated by the DFT B3LYP/6-31G(d) method to 0.30, 0.28, 0.29, and 0.32 kcal mol −1 by the KS-ELM, KS-HC-ELM, and KS-ACO-ELM methods and the artificial neural network (ANN) combined with KS-HC, respectively. This method predicts accurate values with much higher efficiency when compared to the larger basis set DFT calculation and may also achieve similarly accurate calculation results for larger molecules.