Recently, artificial neural networks (NNs) have been applied to construct potential energy surfaces (PESs) in a fully data-driven manner, where the PES is abstracted from a well-selected training dataset using suitable functional expressions . Several formulations, such as DeepMD , GAP , sGDML , and SchNet , have been proposed to develop NN potentials and have achieved success in the modeling of water , small organic molecules , and metal materials . The performance of NN potential depends on the completeness of the training dataset . In other words, NN does well in finding solutions in the function space of the training dataset but might fail in configurations outside the dataset. Therefore, the quality of the dataset is critical to developing NN potentials, and it is recommended to include all the critical configurations during potential reaction processes . MD sampling is the most straightforward way to construct the training dataset, and the evolution of energies and forces are recorded as training datasets . Various bond dissociations and recombinations exist in a reactive system, e.g., the decomposition of HE materials. Such processes cannot be sampled appropriately in classical MD sampling due to the high energy barriers. Enhanced sampling techniques, such as metadynamics , , could improve the sampling quality in the potential reaction coordinates. The concept of interactive molecular dynamics in virtual reality (VRMD) was first proposed by Glowacki et al. and applied to sample the PES of a simple hydrogen abstraction reaction . This method has been proven to accelerate the intelligent curation of high-quality datasets. In a VRMD simulation, VR forces are implemented on atoms to accelerate the reaction processes, similar to metadynamics.