Automated Skeleton Network Generation for ReaxFF Molecular Dynamics Simulations of Hydrocarbon Fuel Pyrolysis and Oxidation via a Rate-Based Algorithm

In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH