As machine learning (ML) PES becomes more and more popular, many sampling methods have been developed for the ML PES models. The uncertainty-driven active learning (AL) sampling is an efficient framework, which iteratively increases the data set by adding the highest uncertainty points into the old set. The sampling algorithm in AUTOSURF can be regarded as one of uncertainty-driven AL sampling if we define the model uncertainty as the difference surface of two L-IMLS models. Such a type of uncertainty can also be employed in the neural network PES sampling. For high-dimensional NN potential, the trajectorybased active learning algorithms are commonly good choice, where the new sets are selected from a large number of trial configurations generated from Molecular Dynamics trajectories. Alternatively, if the system is relatively small, one can directly optimize the uncertainty function to search the local maximum points. This trajectory-free sampling method is proposed by the Bin Jiang group and has achieved good results in reactive PES constructions. However, it is still necessary to comprehensively evaluate its performance in other systems, such as the weakly bounded complex that requires the PES to be accurate in a large amount of configuration space due to its flexible intermolecular vibration.