揭示酸乙烯塑化聚乙烯 (NEPE) 的分解机制:一个神经网络潜力模拟
Mingjie Wen1, Juntao Shi2, Xiaoya Chang1
1State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing, 100081, China. chuqz@bit.edu.cn.
Physical chemistry chemical physics : PCCP
|October 1, 2024
概括
这项研究开发了一种精确的神经网络潜力 (NNP) 模型,用于酸塑化聚乙烯 (NEPE) 推进剂. 该模型准确地预测机械性能和热分解,有助于推进剂的配方.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 酸塑化聚乙烯 (NEPE) 推进剂以其高能量密度和良好的低温性能而闻名.
- 了解微尺度上的NEPE推进剂的热分解至关重要,但目前是有限的.
- 现有的模型缺乏详细的相互作用机制和对NEPE推进剂分解的微观洞察力.
研究的目的:
- 为NEPE矩阵建立一个高精度的神经网络潜力 (NNP) 模型.
- 描述NEPE推进剂的机械行为和详细的热分解机制.
- 为实用推进剂配方设计提供计算工具.
主要方法:
- 为NEPE矩阵开发神经网络潜力 (NNP) 模型.
- 用密度函数理论 (DFT) 验证NNP模型准确性,用于原子能量和力.
- 使用DFT精度的分子动力学 (MD) 模拟来验证NEPE推进剂分解模型.
主要成果:
- NNP模型准确地预测了DFT级精度的NEPE系统的能量和力.
- 该模型复制了与DFT计算一致的机械性能.
- NNP预测的热分解顺序与实验结果一致,捕捉了复杂的现象.
结论:
- 开发的NNP模型为NEPE推进剂模拟提供了DFT级精度.
- 添加结合剂可以提高推进剂的稳定性和能量释放持续时间.
- 这种机器学习方法对于推进NEPE推进剂的配方和设计至关重要.
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