In this work, we present a way to overcome the timescales limitation occurring at low temperatures. Using data at high temperatures, where it is easy to sample many reactions, a kinetic model of hydrocarbon pyrolysis is built at different temperatures and extrapolated to temperatures that are outside the range of the training data. The kinetic model is then run using a Kinetic Monte Carlo algorithm (KMC). This algorithm is particularly suited for running long simulations at low temperatures as the run time of the KMC algorithm is pro-portional to the number of reactions that occurred in the system, not the total time of the simulations. Therefore, at low temperatures, KMC simulations can predict the evolution of the system on timescales that cannot be run in reasonable time with atomistic simulations.