能量材料的爆炸性能和冲击灵敏度 嵌入小分子的NTO:深度神经网络潜在加速分子动力学研究研究
Caimu Wang1,2, Jidong Zhang3, Wei Guo1,2
1Frontiers Science Center for High Energy Material (MOE), Beijing Institute of Technology, Beijing 100081, China. weiguo7@bit.edu.cn.
Physical chemistry chemical physics : PCCP
|September 27, 2024
概括
这项研究引入了一种新的计算方法,以准确预测爆炸性爆炸性能. 该方法提高了对NTO等材料冲击灵敏性的理解,提供了对爆炸物的更安全评估.
科学领域:
- 计算材料科学 计算材料科学
- 化学工程是化学工程的组成部分.
- 物理化学 物理化学
背景情况:
- 准确预测爆炸性爆炸性能至关重要,但目前的方法具有挑战性.
- 现有的实验和理论技术在描述爆炸性行为方面存在局限性.
- 不敏感的爆炸物NTO (2,4,6-三甲) 需要精确的性能评估.
研究的目的:
- 开发和验证一种用于评估爆炸性爆炸性能的新计算框架.
- 评估NTO和相关主机-客户材料的爆炸性能和冲击灵敏度.
- 为爆炸物中冲击灵敏度提出一个新的描述符.
主要方法:
- 结合多尺度冲击技术与第一原则深度神经网络潜力.
- 进行了NTO晶体和宿主-客系统的分子动力学模拟.
- 使用泽尔多维奇-·诺伊曼-多林 (ZND) 模型来确定爆炸参数.
主要成果:
- 成功预测了NTO爆炸速度 (7.9公里/秒) 和压力 (33GPa),与实验数据保持一致.
- 主机-客户材料 (NTO/H2O2,NTO/CO2,NTO/N2O) 显示了比NTO更高的爆炸温度.
- 建立了一个冲击灵敏度顺序:NTO/H2O2 > NTO/N2O > NTO/CO2 > NTO,与材料特性相关.
结论:
- 开发的计算框架准确地预测了爆炸性能和冲击灵敏度.
- 嵌入的小分子通过降低反应障碍和增加热释放来提高冲击灵敏度.
- 建议将达到C-J状态的时间作为爆炸物可靠的冲击灵敏度描述器.
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