The data set (df_cluster) containing the K-means clustering labels is straightforward. Each label corresponds to a specific row in the data set, indicating which cluster the geometry belongs to . By concatenating the K-means labels with the properties dataset, it becomes clear which geometries belong to each cluster, revealing, for instance, that geometry with a large energy gap (around 3.8 eV) belongs to cluster zero, among other insights (first line of output in Figure ). Python simplifies the manipulation of this data, allowing for efficient grouping and analysis of the points within each cluster. Thus, by grouping all rows corresponding to a given cluster, statistics such as the mean value of a given property can be calculated (Figure ). For example, cluster one has an average energy gap of 1.55 eV and contains many hopping geometries. In contrast, cluster zero has essentially no hopping points. This type of analysis enables a deeper understanding of the characteristics within each cluster.