Next, geometries are grouped by similarity in the descriptor space using the clustering module of ULaMDyn (Figure ), which contains different methods similar to the dimensional reduction module. The data set, represented by the descriptor, is provided as input. To reduce the size of the data set and eliminate some correlations, a specific time slice can be given, or a fixed number of samples selected randomly (such as 1,000 geometries from the original data set) can be specified. Based on the color pattern of the ISOMAP plot in Figure (red for large, green for intermediate, and blue for small energy gaps), a reasonable guess for the number of clusters in this demonstration would be three. In cases where this information is not readily available, ULaMDyn offers a built-in function that combines the Silhouette score and Calinski-Harabasz 65 metric to determine the optimal number of clusters. This feature can be accessed by setting the number of clusters parameter to 'best.'