Core electrons are treated using Goedecker-Teter-Hutter (GTH) pseudopotentials and the Perdew Burke Ernzerhof (PBE) generalized gradient approximation method . The Grimme DFT-D3 method is used to account for dispersion interactions. A double-zeta Gaussian basis set plus polarization (DZVP-MOLOPT) is considered. In addition to MD simulations, further configurations are obtained using active learning sampling implemented in the DP-GEN package . In the stage of active learning, four NNPs are firstly trained with different random seeds. Then, MD simulations with a temperature range from 300 to 4000 K are performed using one of the NNPs. The MD trajectories are evaluated by the other three NNPs to obtain the deviation of atomic forces, which is used as a criterion to identify the new configurations. The configurations with relative deviation in the range of 0.05-0.15 are added to the training set. In total, we perform 20 iterations of active learning to derive an accurate NNP for the decomposition of RDX crystal (Table ). The iterations 1-13 investigate the RDX decomposition process under an NVT ensemble with a 2x2x2 supercell. The temperature gradually increases from 300 K to 4000