Figure illustrates the basic procedure of the sampling method in this work. The first step is to generate the initial set via the Sobol quasirandom method. This strategy doesn't require any previous knowledge about the PES and would cover the given configuration space more evenly. Then, several neural network models would be trained with the initial set. The initial trainable parameters of these NNs are different, and several sets of training hyper-parameters are employed to find the best-fitting models as well as possible. Two NN PESs that have the lowest training errors would be selected for uncertainty calculation. The weighted squared difference surface D(x) of these two PESs would be the uncertainty in the configuration space: 26