Uncovering the decomposition mechanism of nitrate ester plasticized polyether (NEPE): a neural network potential simulation.

Nitrate ester plasticized polyether (NEPE) propellants have attracted widespread attention due to their high energy density and excellent low-temperature mechanical properties. However, little is known about the thermal decomposition process of the NEPE propellant, particularly lacking microscale models and interaction mechanisms. This work aims to establish a high-precision and efficient neural network potential (NNP) model covering the NEPE matrix, describing its mechanical behavior and detailed thermal decomposition mechanisms. The model accuracy, including atomic energies and forces, was validated through density functional theory (DFT) results, and the NEPE propellant decomposition model was verified molecular dynamics (MD) simulations with DFT precision. The results demonstrate that the NNP model accurately predicts the energies and forces of the NEPE matrix for single and mixed systems at the DFT-level precision, and reproduces the mechanical properties consistent with DFT calculations. Meanwhile, the thermal decomposition order of the NEPE matrix predicted by NNP is consistent with the experimental results, accurately capturing complex physical phenomena and detailed decomposition processes among components. It is also revealed that the addition of a binder can improve the stability of the propellant and extend its energy release time. This study applies innovative machine learning algorithms to develop an NNP computational model for the NEPE matrix with DFT precision, which is crucial for practical propellant formulation design.