  Understanding and prediction of the chemical reactions are fundamental
demanding in the study of many complex chemical systems. Reactive molecular
dynamics (MD) simulation has been widely used for this purpose as it can offer
atomic details and can help us better interpret chemical reaction mechanisms.
In this study, two reference datasets were constructed and corresponding neural
network (NN) potentials were trained based on them. For given large-scale
reaction systems, the NN potentials can predict the potential energy and atomic
forces of DFT precision, while it is orders of magnitude faster than the
conventional DFT calculation. With these two models, reactive MD simulations
were performed to explore the combustion mechanisms of hydrogen and methane.
Benefit from the high efficiency of the NN model, nanosecond MD trajectories
for large-scale systems containing hundreds of atoms were produced and detailed
combustion mechanism was obtained. Through further development, the algorithms
in this study can be used to explore and discovery reaction mechanisms of many
complex reaction systems, such as combustion, synthesis, and heterogeneous
catalysis without any predefined reaction coordinates and elementary reaction
steps.
