Second, we collected snapshots of every two nuclear steps (with a time separation of 1 fs) in all the above dynamics to form our dataset library, with a total of 166000 azomethane conformations). We randomly chose 120000 azomethane conformations to build the preliminary training dataset and chose the rest of 46000 configurations to build the testing dataset. As one can see, the training dataset has a huge number of data points. On the one hand, if we used all of these data in the KRR regression, the fitting procedure may become extremely slow and the regression process will require a huge amount of computer memory. On the other hand, in the sampling process, several data points may be located within the same areas, while much fewer points in some other areas. Thus, it is useful to create a balanced description of the data distribution over all important spaces in the regression, as suggested by previous work. The most direct way is to perform the clustering analysis of data points before training.