The distribution of the energy gap for each cluster can be visualized using a histogram (Figure ). This helps illustrate the differences in the energy gap across the clusters. Despite the energy gap not being included in the descriptor used for clustering, its distinct distribution among clusters underscores the effectiveness of the data partitioning algorithm, which serves as a validation step, confirming that the clustering results are meaningful. For example, clusters zero and one exhibit distinct energy gap distributions, highlighting their unique characteristics. On the other hand, clusters one and two show some overlap in the region of small energy gaps. This observation aligns with the findings, which identified two different types of geometries associated with hopping events: one related to bond stretching and another linked to the rotation of the CH₂ groups. To further assess the effectiveness of the clustering algorithm, dimensionality reduction was applied to the differential Z-Matrix data set. The two primary components were plotted, and the points were colored according to their cluster assignments to visually evaluate the clustering results (Figure ).