Unsupervised Machine Learning. Expanding on the simulation parameters from the histogram cluster approach led to decreasing the timestep from 4 fs to 1 fs, increasing the simulation time to 10 ns. With snapshots collected every 5 ps, this would allow for more structures to be included in the conformational search. The challenge molecules were extracted from the frames of the MD trajectory and converted for use with the RDKit and scikit-learn Python toolkits with the xyz2mol code. In total, 5000 structures were generated for each molecule in water and toluene. A pairwise nucleus-nucleus distance lower triangular matrix was the descriptor used to characterize the 3D representation of the molecule. Given the high dimensionality of the data, the data was standardized using the standard scalar and then principal component analysis (PCA) was used for pre-processing to reduce the data to three principal components. A Gaussian Mixture Model was used to identify unique clusters. The optimal number of clusters were calibrated via elbow curves and maximizing the silhouette score when using the K-means clustering algorithm. The number of clusters predicted with both the K-means clusters and the Gaussian Mixture models was verified via five-fold cross validation using varying sizes of training/test splits (see ESI for details). For each proposed cluster, the five data points that yielded the smallest magnitude of distance to the cluster mean were chosen as representative structures to sample the cluster. After verification of rotamer structures via the identified structures, the logPtol/w was computed using the density functional and basis set combination that yielded the lowest mean unsigned error (MUE) with respect to the experimental logPtol/w values.