Wen Mingjie, Shi Juntao, Chang Xiaoya, Han Jiahe, Pang Kehui, Chen Dongping, Chu Qingzhao
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing, 100081, China.
National Key Laboratory of Aerospace Chemical Power, Xiangyang, Hubei Province, 441000, China.
Phys Chem Chem Phys. 2024 Oct 9;26(39):25719-25730. doi: 10.1039/d4cp02223h.
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.
硝酸酯增塑聚醚（NEPE）推进剂因其高能量密度和优异的低温力学性能而受到广泛关注。然而，人们对NEPE推进剂的热分解过程了解甚少，尤其缺乏微观尺度模型和相互作用机制。这项工作旨在建立一个涵盖NEPE基体的高精度、高效神经网络势（NNP）模型，描述其力学行为和详细的热分解机制。通过密度泛函理论（DFT）结果验证了包括原子能量和力在内的模型精度，并利用具有DFT精度的分子动力学（MD）模拟对NEPE推进剂分解模型进行了验证。结果表明，NNP模型在DFT水平精度下准确预测了单体系和混合体系中NEPE基体的能量和力，并再现了与DFT计算一致的力学性能。同时，NNP预测的NEPE基体热分解顺序与实验结果一致，准确捕捉了复杂的物理现象和各组分之间的详细分解过程。研究还表明，添加粘结剂可以提高推进剂的稳定性并延长其能量释放时间。本研究应用创新的机器学习算法开发了具有DFT精度的NEPE基体NNP计算模型，这对实际推进剂配方设计至关重要。
