The overall performance of the NN potential is tested against the ab initio database. Figure shows the prediction of DFT energies and forces using the NNP trained by the DP scheme. The results predicted from the ReaxFF model are also included for comparison. The energies predicted from the NNP cluster on the diagonal in We also evaluate the computational costs of NNP, ReaxFF, and DFT methods on the bulk RDX systems with 100 to 100,000 atoms (Figure ). The neural network potential-based molecular dynamics simulations (NNP-MDs) are performed on an NVIDIA V100 GPU. The ReaxFF and DFT calculations are solved on a 64-processor server with two AMD EPYC 7452 CPUs. The NNP shows a linear scaling rule in the computational cost, which is 27 times faster than the ReaxFF model. Compared to the AIMD method, the simulations using NNP are faster by four orders of magnitude. This significant improvement in the computational costs is consistent with previous works using the NNPs . This can be attributed to the implementation in the neural network combining the-state-of art GPU computing. The computing efficiency of the NNP in small systems with hundreds of atoms deviates from other significant cases. This is expected as the implementation of the NNP is optimized for large systems. Also, it is worth noting that the ReaxFF model exhibits an O(N) scaling rule rather than an O(NlogN) scaling rule, different from previous works in the low-density gas . The better performance of the NNP enables the exploration of a system with tens of thousands or even millions of atoms at the ab initio accuracy, providing a feasible approach to investigate the complex reaction network of RDX crystal from an atomic perspective.