An <i>ab initio</i> deep neural network potential to study the effect of density on the thermal decomposition mechanism of FOX-7

Condensed phase explosives typically contain defects such as voids, bubbles, and pores; this heterogeneity facilitates the formation of hot spots and triggers decomposition reaction at low densities. The study of the thermal decomposition mechanisms of explosives at different densities has thus attracted considerable research interest. Gaining a deeper insight into these mechanisms would be helpful for elucidating the detonation processes of explosives. In this work, we developed an ab initio neural network potential for the FOX-7 system using machine learning method. Extensive large-scale (1008 atoms) and long-duration (nanosecond timescale) deep potential molecular dynamics simulations at different densities were performed to investigate the effect of the density on the thermal decomposition mechanism. The results indicate that the initial reaction pathway of the FOX-7 explosives is the cleavage of the C-NO2 bond at different densities, while the frequency of C-NO2 bond cleavage decreases at higher density. Increasing the initial density of FOX-7 significantly increases the reaction rate during the initial decomposition and the formation of final products. However, it leads to a decrease in released heat and has minimal impact on the decomposition temperature. In addition, by analyzing the molecular dynamics trajectories and conducting quantum chemical calculations, we identified two lower-barrier production pathways to produce the CO2 and N2.