Gaps between the MD simulations and experiments still exist in explosive decomposition reactions. Although the landscape of ICM-102 molecules has been drawn via AIMD and VR-enhanced sampling, further work is required to assemble reaction networks into kinetics models to interpret macro-phenomena in diverse fields, such as combustion, catalysis, and atmospheric chemistry. There are many wellestablished methods, such as model reduction and sensitivity analysis, to construct kinetic models and describe the macroproperties. Importantly, the present work constructs a novel VR-enhanced sampling method to train NN potentials and enables MD simulations of a novel high explosive (i.e. ICM-102) with complex reaction networks at the level of ab initio calculations, providing atomic insights into the detailed reaction pathways that are otherwise difficult to uncover, such as ring-opening reactions and the formation of N2, CHNO, and CO2. Thus, the study opens up new possibilities to build reaction kinetics models based on high-fidelity and low-cost AI algorithms.