In this work, we took the similarity between the Coulomb matrix of different geometries as the reference to perform clustering with the hierarchical agglomerative clustering algorithm. The training dataset was clustered into 1000 groups. Since the data is not well distributed, when we cluster them into subgroups, the number of data in each subgroup varies from each other. The number of data in some subgroups is less than 100, in this case, we selected all the data (geometries) in these subgroups. Otherwise, we selected at most 100 geometries from each group. All selected geometries formed the final training dataset, which contains a total of 72993 azomethane conformations. The wide distribution of the CNNC dihedral angle (see Fig. in the Supporting Information), which is the main reaction coordinate during the isomerization process, indicates that the training dataset is properly sampled in this work.