基于机器学习预测反应类的大型ReaxFF MD热解模拟反应的骨架反应网络的生成
Shanwen Yang1,2, Xiaoxia Li1,2,3, Mo Zheng1,2,3
1State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China. xxia@ipe.ac.cn.
Physical chemistry chemical physics : PCCP
|January 30, 2024
概括
本研究介绍了SRG-Reax,这是一种使用机器学习简化复杂热解反应的新方法,来自ReaxFF模拟. 它产生了骨反应网络,以更好地了解燃料化学.
科学领域:
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
背景情况:
- 现实燃料混合物的热解产生复杂的反应网络.
- 鉴定这些反应是具有挑战性的,因为多个网站和不同的途径.
研究的目的:
- 开发SRG-Reax (Skeleton Reaction network Generation for ReaxFF MD) 用于生成简化的反应网络. 这是一个非常简单的过程.
- 使用ReaxFF分子动力学分析现实燃料的热解化学.
主要方法:
- 实施了一种三培训的半监督机器学习模型.
- 利用反应中心的描述,特征和转换指纹作为输入.
- 应用SRG-Reax到ReaxFF MD模拟RP-3燃料热解.
主要成果:
- 为RP-3燃料中的各种成分 (n-paraffins,iso-paraffins,cycloparaffins,olefins,aromatics) 生成了骨架反应网络.
- 通过反应类和产品物种比率确定主要反应途径.
- 提供了对全球热解化学的全面见解.
结论:
- SRG-Reax有效地简化了复杂的热解反应数据.
- 这种方法有可能为燃料,聚合物,生物质和煤炭等各种材料生成骨架网络.
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