Detonation performance and shock sensitivity of energetic material NTO with embedded small molecules: a deep neural...

Accurate description of detonation performance for explosives remains a challenge for current experimental and theoretical methodologies. Herein, we address this issue through combining a multi-scale shock technique and a first-principles based deep neural network potential. This approach enables us to conduct molecular dynamics simulations encompassing over a thousand atoms and extending for several nanoseconds, allowing us to evaluate the detonation performance of the insensitive explosive NTO crystal. Utilizing the ZND model, we successfully determine the detonation velocity (7.9 km s^-1), and detonation pressure (33 GPa) of the NTO crystals at the C-J state, which align well with experimental results. Additionally, we predict the detonation performance of three host-guest materials: NTO/H₂O₂, NTO/CO₂, and NTO/N₂O, all of which exhibit higher detonation temperatures compared to the NTO crystals in the present model. Moreover, we proposed the time to reach the C-J state as a shock sensitivity descriptor for explosives. Our findings reveal that the order of shock sensitivity for these materials is NTO/H₂O₂ > NTO/N₂O > NTO/CO₂ > NTO, and the trend can be explained in terms of bulk modulus, electronic band gap and oxygen balance. The enhanced shock sensitivity by embedded small molecules arises not only from the reduction in initial reaction barriers, but also from the faster evolution rate of final products and the release of more heat. Our research might present a cutting-edge framework for precisely, quickly, and safely evaluating and modulating the detonation performance and shock sensitivity of explosives.