Search Results (3)

The initial channels of thermal decomposition mechanism of 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105) molecule were investigated. The results of quantum chemical calculations revealed four candidates involved in the reaction pathway, including the C–NO₂ bond homolysis, nitro–nitrite rearrangement followed by NO elimination, and H transfer from amino to acyl O and to nitro O with the subsequent OH or HONO elimination, respectively. In view of the further kinetic analysis and ab initio molecular dynamics simulations, the C–NO₂ bond homolysis was suggested to be the dominant step that triggered the decomposition of LLM-105 at temperatures above 580 K. Below this temperature, two types of H transfer were considered as the primary reactions, which have advantages including lower barrier and high rate compared to the C–NO₂ bond dissociation. It could be affirmed that these two types of H transfer are reversible processes, which could buffer against external thermal stimulation. Therefore, the excellent thermal stability of LLM-105, that is nearly identical to that of 1,3,5-triamino-2,4,6-trinitrobenzene, can be attributed to the reversibility of H transfers at relatively low temperatures. However, subsequent OH or HONO elimination reactions occur with difficulty because of their slow rates and extra energy barriers. Although nitro–nitrite rearrangement is theoretically feasible, its rate constant is too small to be observed. This study facilitates the understanding of the essence of thermal stability and detailed decomposition mechanism of LLM-105. Full article

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. Full article

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 full-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. Full article