This metric can then be used together with clustering algorithms, 47 a type of unsupervised machine learning. We choose k-medoids clustering, which represents each cluster by its "medoid", i.e., the structure that minimizes the sum of distances to all other structures in the same cluster. The only human bias is the choice of the number of clusters k. However, there are different heuristics for choosing k in an automated manner. Computational details for all our simulations can be found in the supporting information. In order to illustrate our approach, we performed an MCMC simulation for 3 million steps starting from an asymmetric start configuration, based on the joint probability density of a non-BO molecular wavefunction of the D + 3 molecule, one of the homonuclear H + 3 isotopomers studied by Cafiero and Adamowicz. In order to demonstrate that the sample size is sufficient, we computed expectation from the sample and compared them with analytical values; see Table . Some snapshots from this simulation are shown points, the k-medoids algorithm cannot be performed on the full set of 3 million snapshots.