In the past two decades, ab initio molecular dynamics (AIMD) simulations were performed to investigate the initial decomposition pathways of HMX , CL-20 , and NTO 14 molecules. However, these works were limited to a small system (i.e., <300 atoms), and a short reaction time (i.e., <50 ps). Despite impressive progress in computing hardware and software in recent decades, AIMD calculations on the complete reaction process of RDX crystal are still challenging due to the high computational cost. To alleviate the demand for computing power, empirical potentials (or force fields) were developed to construct the potential energy surface (PES) from DFT calculations. 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 prior knowledge of the predefined reactive sites. It is a powerful tool for studying kinetic mechanisms for large molecules and complex reactions . However, the computational accuracy of the ReaxFF model is relatively limited due to the underlying functional forms. Recently, machine learning-based tools, especially neural networks (NNs), have been applied to construct PES models in an entirely data-driven 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, enabling the development of PES models with the efficiency of the empirical potentials and the accuracy of the DFT method. A few successful NN-based potentials (NNPs) are proposed for materials and biomolecules , which can accurately reproduce the interatomic forces and energies predicted by ab initio calculations in condensed matters. In particular, Cao et al. implemented an NNP to reveal the mechanisms of CL-20/TNT co-crystal. They found that the TNT molecules in the co-crystal act as a buffer to slow down the chain reactions triggered by nitrogen dioxide, which is more significant at lower temperatures.