Abstract
Against the backdrop of insufficient research into the microscopic reaction mechanisms of pentazole anion ((Formula presented.)) salts, the present study developed a deep neural network potential (DNNP) model calibrated with first principles data. On this basis, large-scale molecular dynamics (MD) simulations were performed to conduct an in-depth investigation into the thermal decomposition mechanism and kinetic processes of hydroxylamine pentazole (NH3OHN5) at the atomic scale. A highly precision DNNP model was constructed using an active learning strategy, whose predictions for energy and atomic forces showed excellent agreement with Density Functional Theory (DFT) results. MD simulations revealed that the thermal decomposition of NH3OHN5 initiates with a hydrogen transfer reaction. The protonation of the (Formula presented.) reduces its ring-opening energy barrier from 125.45 to 112.13 kJ/mol, significantly promoting the ring-opening decomposition process. The final decomposition products were predominantly N2, H2O, and NH3. This research elucidates the decomposition pathways and reaction mechanism of NH3OHN5 at the atomic scale, demonstrating the exceptional capability of the DNNP in simulating the reaction dynamics of energetic materials and providing a theoretical foundation for the subsequent molecular design of high-performance, green energetic materials.
| Original language | English |
|---|---|
| Article number | e70362 |
| Journal | Journal of Computational Chemistry |
| Volume | 47 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 30 Mar 2026 |
Keywords
- energetic materials
- ionic salts
- machine learning
- pentazole
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