The complexity of the chemical and physical reactions during soot nucleation makes it both numerically and experimentally challenging. Ab initio molecular dynamics (AIMD) simulations have been widely applied to reveal the atomic insights of complex reactive systems based on fundamental equations of quantum and classical mechanics. Although AIMD has been successfully used for polyyne inception and PAH reactive dimerization , AIMD calculations of large PAH molecules/radicals are still challenging due to the high computational cost despite impressive progress in computing hardware and software in recent decades. Over the past decade, many empirical potentials (or force fields) have been developed to mimic electronic structure calculations' potential energy surface (PES). Such empirical potentials, including ReaxFF and REBO , trade accuracy for a lower computational expense, making it possible to extend simulation scales to orders of magnitude beyond AIMD methods. ReaxFF is a bond order-based force field that describes reactive systems without a priori knowledge of the predefined reactive sites. It has been a powerful tool to study kinetic mechanisms for systems of large molecules and complex reactions. Recently, machine learning-based tools, especially neural networks (NNs), have been applied to construct PES models in an entirely datadriven manner, where the PES is abstracted from a well-selected training dataset using suitable functional expressions automatically . NN models constitute a very flexible class of mathematical functions, which enable the development of PES models with the efficiency of the empirical potentials and the accuracy of the DFT method. Different NN-based PES models have been proposed for materials and biomolecules . Zeng et al. implemented an NN-based model to reveal the mechanisms of methane combustion.