Revealing the thermal decomposition mechanism of RDX crystals by a neural network potential
QZ Chu and XY Chang and K Ma and XL Fu and DP Chen, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 24, 25885-25894 (2022).
A neural network potential (NNP) is developed to investigate the complex reaction dynamics of 1,3,5-trinitro-1,3,5-triazine (RDX) thermal decomposition. Our NNP model is proven to possess good computational efficiency and retain the ab initio accuracy, which allows the investigation of the entire decomposition process of bulk RDX crystals from an atomic perspective. A series of molecular dynamics (MD) simulations are performed on the NNP to calculate the physical and chemical properties of the RDX crystal. The results show that the NNP can accurately describe the physical properties of RDX crystals, such as the cell parameters and the equation of state. The simulations of RDX thermal decomposition reveal that the NNP could capture the evolution of species at ab initio accuracy. The complex reaction network was established, and a reaction mechanism of RDX decomposition was provided. The N-N homolysis is the dominant channel, which cannot be observed in previous DFT studies of isolated RDX molecule. In addition, the H abstraction reaction by NO2 is found to be the critical pathway for NO and H2O formation, while the HONO elimination is relatively weak. The NNP gives an atomic insight into the complex reaction dynamics of RDX and can be extended to investigate the reaction mechanism of novel energetic materials.
Return to Publications page