Recently, building ab initio based high-dimensional potential energy surface (PES) using a neural network (NN) approach has become popular since the work of Behler and Parrinello . Running MD simulation on neural network potential energy surface (NNPES) can significantly increases computational efficiency in comparison to the standard AIMD approach as one does not need to calculate the electronic structure at every time step. Such NNPES based MD simulations have been used to study the properties of small organic molecules, liquids, materials, etc. . In addition, NNPES based MD simulation can also be used to explore chemical reactions. Recently, elementary reactions in the gas phase and on the surface have been studied using NNPES . In our previous work, nanosecond-scale reactive MD simulations with NNPES were performed to explore detailed reaction network for methane combustion and pyrolysis of linear alkanes .