ReaxFF Reactive Force Field for Molecular Dynamics Simulations of Hydrocarbon Oxidation

雷亚克夫 化学 丙烯 分子动力学 甲烷 碳氢化合物 氢原子萃取 计算化学 力场(虚构) 光化学 有机化学 催化作用 计算机科学 人工智能 原子间势
作者
Kimberly Chenoweth,Adri C. T. van Duin,William A. Goddard
出处
期刊:Journal of Physical Chemistry A [American Chemical Society]
卷期号:112 (5): 1040-1053 被引量:2609
标识
DOI:10.1021/jp709896w
摘要

To investigate the initial chemical events associated with high-temperature gas-phase oxidation of hydrocarbons, we have expanded the ReaxFF reactive force field training set to include additional transition states and chemical reactivity of systems relevant to these reactions and optimized the force field parameters against a quantum mechanics (QM)-based training set. To validate the ReaxFF potential obtained after parameter optimization, we performed a range of NVT-MD simulations on various hydrocarbon/O2 systems. From simulations on methane/O2, o-xylene/O2, propene/O2, and benzene/O2 mixtures, we found that ReaxFF obtains the correct reactivity trend (propene > o-xylene > methane > benzene), following the trend in the C-H bond strength in these hydrocarbons. We also tracked in detail the reactions during a complete oxidation of isolated methane, propene, and o-xylene to a CO/CO2/H2O mixture and found that the pathways predicted by ReaxFF are in agreement with chemical intuition and our QM results. We observed that the predominant initiation reaction for oxidation of methane, propene, and o-xylene under fuel lean conditions involved hydrogen abstraction of the methyl hydrogen by molecular oxygen forming hydroperoxyl and hydrocarbon radical species. While under fuel rich conditions with a mixture of these hydrocarbons, we observed different chemistry compared with the oxidation of isolated hydrocarbons including a change in the type of initiation reactions, which involved both decomposition of the hydrocarbon or attack by other radicals in the system. Since ReaxFF is capable of simulating complicated reaction pathways without any preconditioning, we believe that atomistic modeling with ReaxFF provides a useful method for determining the initial events of oxidation of hydrocarbons under extreme conditions and can enhance existing combustion models.
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