Generating a skeleton reaction network for reactions of large-scale ReaxFF MD pyrolysis simulations based on a machine learning predicted reaction class.

Abstract

The reactive molecular dynamics using ReaxFF provides an effective means to generate global reactions for pyrolysis of realistic fuel mixtures. The reactions from large-scale pyrolysis simulations of a fuel mixture may be characterized by multiple reaction sites, explosion of intermediate species structures, and scattered contribution of diversified pathways to product species. This work proposes an approach of SRG-Reax aiming at generating skeleton reaction networks based on reaction patterns or classes of reaction centers from huge reactions obtained from ReaxFF MD simulations of realistic fuel pyrolysis. SRG-Reax (Skeleton Reaction network Generation for ReaxFF MD) is implemented through building a semi-supervised machine learning model of tri-training for predicting the reaction classes of pyrolysis reactions based on an extended reaction center. Three different reaction center descriptions of reaction features and reaction transformation fingerprints are employed as inputs for developing the tri-training classifier. Major reaction pathways can be identified based on reaction class ratios and product species ratios calculated by merging reaction pathways of the same reaction class. The SRG-Reax approach was applied in skeleton reaction network generation for RP-3 pyrolysis based on the ReaxFF MD simulations of a high-fidelity 45-component RP-3 fuel model. The skeleton reaction networks for n-paraffins, iso-paraffins, cycloparaffins, olefins, and aromatics in RP-3 pyrolysis were obtained. The reaction class ratios and product species ratios in the obtained skeleton reaction network provide comprehensive intuitive insight into global pyrolysis chemistry. SRG-Reax has the potential to obtain relatively complete skeleton reaction networks for the pyrolysis of hydrocarbon fuel, polymers, biomass, coal, and more.