AIMD and VR enhanced sampling. with a small number of additional configurations. We believe such a reduced dataset is extremely valuable to the application of supervised learning in the field of computational chemistry because dataset labeling is the most time-consuming step in supervised learning, corresponding to electronic structure calculations in NN potential training. A small improvement in accuracy requires a great effort in computational resources. Recently, it has been found that the computational efforts at high-level ab initio calculations can be minimized by combining datasets of molecular forces from different levels of theory . However, such a strategy is highly dependent on the wellselected training set of high-level ab initio calculations. We are working on integrating the VR-enhanced sampling algorithms to develop NN potentials with a high level of accuracy.