Thermal Decomposition of 1,1-Diamino-2,2-dinitroethylene Using a Chemical Reaction Neural Network: Kinetic Modelling and Reaction Mechanism Analysis

1,1-Diamino-2,2-dinitroethylene (FOX-7) is a high-energy, low-sensitivity explosive, yet its decomposition pathway remains critical for safe application. In this study, the thermal decomposition of FOX-7 was investigated through a combination of thermogravimetric (TG) measurements and chemical reaction neural network (CRNN) modelling. Five sets of the experimental TG measurements were first selected to evaluate the inherent uncertainties. In particular, the two-stage decomposition characteristics and the solid residue were discussed in detail. Two CRNN models. i.e., the 5-2 model (five species and two reactions) and 5-4 model (five species and four reactions) were developed, with both accurately predicting initial decomposition activation energies. The 5-4 model elucidates detailed reaction pathways, including C─NO₂, C═C, and C─H bond cleavages, alongside product interactions, aligning with prior theoretical studies. The overall reaction mechanism and the associated energy barriers for bond dissociation are consistent with previous theoretical studies. Our findings highlight the capability of the CRNN model to decode complex decomposition kinetics, including multi-stage reactions and residue formation. This approach offers a promising framework for modelling other energetic materials.