In this example, the K-means algorithm split the data into three clusters labeled 0, 1, and 2, where cluster two contains significantly fewer geometries compared to the others. Upon closer inspection and analysis, we observe that this smaller cluster contains geometries related to CH 2 rotations. This is consistent with the finding that such rotated geometries are less frequently visited during the dynamics compared to other points. Consequently, the smaller cluster size reflects the lower occurrence of these rotated geometries.