The first approach exploited an unsupervised machine-learning algorithm (see Fig. ). In this case, clustering was based on dimensionality reduction with KPCA of the SOAP descriptors. In the following, clusters are labeled with a progressive number from the most to the least recurrent one: more precisely, cluster 1 encompasses 62 structures, while only two conformers are grouped into cluster 9.