Clustering methods are commonly used to make sense of these large datasets, helping to organize chemical systems into subgroups with shared electronic properties or spatial configurations. Unlike dimensionality reduction, which compresses data into a smaller set of critical components and may create artificial groupings, clustering identifies natural subgroups in the original data space. This preserves the intrinsic relationships and structures within the data, making it particularly useful for identifying families of molecules with similar electronic properties or revealing patterns in chemical composition and spatial configurations essential for understanding molecular interactions and reactivity. A well-tuned clustering algorithm distills large volumes of data into a manageable number of qualitatively distinct categories, facilitating data visualization and exploration. This approach also improves the efficiency of further computational and experimental studies by enabling a more focused analysis of nonadiabatic dynamics, thereby enhancing our understanding of excited -state processes and transitions.