用于ReaxFF的自动化骨架网络生成 碳化合物燃料热解和氧化分子动力学模拟通过以速率为基础的算法
Yuanyuan Xiao1,2,3, Mo Zheng1,2,3, Xiaoxia Li1,2,3
1State Key Laboratory of Mesoscience and Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China.
Journal of chemical theory and computation
|June 28, 2024
概括
我们开发了一种自动化方法,RxMD-SN,从分子动力学模拟中生成碳化合物燃料的反应动力学机制. 这种方法有效地捕捉复杂的反应路径,有助于研究燃料氧化和热解.
科学领域:
- 计算化学的计算化学
- 化学动力学 化学动力学
- 分子动力学分子动力学
背景情况:
- 对于大型碳化合物燃料来说,导出详细的反应动力机制在计算上具有挑战性.
- 现有的方法难以捕捉复杂系统中罕见但重要的反应事件.
- 准确的动力学模型对于理解燃料燃烧和热解至关重要.
研究的目的:
- 介绍一种自动化的方法,RxMD-SN,用于从ReaxFF MD模拟中生成骨架反应网络.
- 为了使大型碳化合物燃料的反应动力学机制得出.
- 为了提高化学动力学建模的效率和准确性.
主要方法:
- 开发了一个基于速率的算法,用于骨架网络生成 (RxMD-SN).
- 从大规模的ReaxFF MD模拟中利用了反应速率常数的统计计算.
- 采用基于时间反应速率的反应流量排名来提取核心网络,包括罕见事件.
主要成果:
- 通过RxMD-SN方法成功生成了CH4氧化的动力模型,该模型重现了ReaxFF MD模拟结果.
- 证明了RxMD-SN在捕获全球反应动力学方面的能力.
- 在不同的碳化合物燃料 (n-octane,n-heptane) 和反应类型 (氧化,热解) 中确定了共享的动力参数.
结论:
- RxMD-SN是一种强大的工具,用于描述大型碳化合物分子的氧化动力学.
- 该方法提供了基于ReaxFF MD模拟的化学动力学建模的一般方法.
- 这种方法有助于在燃料科学中研究复杂的反应网络.
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