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.

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.

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−1calculated by the DFT B3LYP/6-31G(d) method to 0.30, 0.28, 0.29, and 0.32 kcal mol−1by 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.

Building molecular complexity from simple feedstocks through peripheral and skeletal editing is central to modern organic synthesis. Nevertheless, a controllable strategy that can modify both the core skeleton and periphery of an aromatic heterocycle with a common substrate remains undeveloped, despite its potential to maximize structural diversity and applications. Here we report a fluoroalkyl carbene-initiated chemodivergent molecular editing of indoles, allowing both skeletal and peripheral editing by trapping electrophilic fluoroalkyl carbene generated in situ from fluoroalkyl N-triftosylhydrazones. A variety of fluorine-containing N-heterocyclic scaffolds could be efficiently achieved through the tunable chemoselective editing reactions at skeleton or periphery of the indole, including one-carbon insertion, C3−H gem-difluoroolefination, tandem cyclopropanation/N1−H gem-difluoroolefination, and cyclopropanation. Mechanistic experiments and computations have probed the reaction mechanism and the origins of chemo- and regioselectivity.

Seven novel energetic 4,7-dinitro-furazano-[3,4-d]-pyridazine derivatives were designed, and their optimized structures and performances were studied by density functional theory (DFT) at B3LYP/6-311g(d,p) level. The detonation performances were estimated by the Kamlet-Jacobs equations. The results show that these compounds have high crystal densities (1.818-1.925 g·cm , and some of them exhibit higher BDEs than that of RDX (N-NO2 bond, 149.654 kJ·mol , 39.00 GPa). Otherwise, the specific impulse values of M1-M7 (266-279 s) outperform HMX (266 s) by 0-13 s, which indicates that M1-M7 may show better performance as monopropellants. It is concluded that density, heat of formation, stability, detonation performance and specific impulse of the designed compounds depend on the position and number of the N→O oxidation bonds.