RDX/AP复合物的热分解机制:ab initio神经网络MD模拟
Kehui Pang1, Mingjie Wen1, Xiaoya Chang1
1State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China. chuqz@bit.edu.cn.
Physical chemistry chemical physics : PCCP
|March 27, 2024
概括
一个新的神经网络潜力 (NNP) 准确地模拟了RDX和AP能量材料的分解. 这种NNP揭示了双重相互作用,其中AP加速RDX分解,RDX促进AP分解.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 了解RDX和AP等高能材料的分解机制对于安全性和性能至关重要.
- 像ReaxFF这样的现有模型对复杂的复合系统的准确性和稳定性有局限性.
研究的目的:
- 开发和验证一个神经网络潜力 (NNP) 来模拟RDX,AP及其复合物的分解.
- 在原子层面研究RDX和AP之间的合效应和反应动力学.
主要方法:
- 在*ab initio*数据集上训练的神经网络潜力 (NNP) 的开发.
- 对原子能和原子力的NNP精度的评估,以及数值稳定性测试.
- 使用NPN模拟纯RDX,AP和RDX/AP复合物的热分解.
主要成果:
- 与ReaxFF相比,NNP表现出与*ab initio*计算的强烈一致以及优越的数值稳定性.
- 观察到一种双重相互作用:AP加速RDX分解 (特别是在低温下),而RDX促进AP分解.
- 在RDX/AP接口上的RDX分解涉及N-N键裂解,H转移与AP,以及链断反应,AP增强H转移.
结论:
- 开发的NNP提供了一种稳定而准确的方法来研究能量材料分解.
- 这些发现阐明了RDX/AP复合材料中的协同效应,为它们的反应动力学提供了洞察力.
- 这项工作有助于为复合能量材料开发反应动力学模型.
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