The performance of the NN potential is tested against the ab initio database. Figure shows the prediction of DFT energies and forces using the NN potential trained by the DP scheme. The results predicted from the ReaxFF forcefield are also listed for comparison. Detailed mean absolute errors (MAEs) are listed in Table . We found that the MAE values increase with the system temperature in MD sampling. The DP model shows a lower error than the ReaxFF model (0.67 v.s. 1.18 kcal/mol/atom), suggesting the good fitting ability of the DP scheme. Surprisingly, we find that the DP model shows excellent performance for force prediction, while the ReaxFF model seriously underestimates the interatomic forces (38.62 kcal/mol/Å). This can be explained by the parameterization of ReaxFF, as only energy information is taken as the training targets. By considering the forces in loss function (Eq. 1), the DP model significantly improves the model prediction and, therefore, accurately captures the dynamic evolution of the soot formation process. We also test the computational cost of DP and ReaxFF potentials on C2H2 systems with 20 to 160,000 atoms. The NNMD simulations of the DP model are performed with one NVIDIA V100 GPU, and the ReaxFF MD simulations are on a 64-processor server with two AMD EPYC 7452 CPUs. Figure shows that our DP model follows an O(N) scaling rule, while ReaxFF exhibits an O(NlogN) scaling rule. The better scaling performance of the DP model enables the exploration of a system with tens of thousands or even millions of atoms at ab initio accuracy, providing a possibility to investigate the entire evolution of soot particles from an atomic perspective. Fig. . The computational cost of DP (red square) and ReaxFF (blue circle) models on C 2 H 2 systems with 20 to 160,000 atoms. The system lengths are from 1 to 28 nm. The insert shows snapshots of the corresponding systems.