For each molecule, conformers were generated using the CREST program to exhaustively sample the potential energy surface, using default parameters and the GFN2-xTB method to compute energies (hereafter as simply GFN2) and optimize geometries. In some cases, CREST produced fragments or chemical rearrangements (e.g., producing different compounds than the input, based on the InChI identifier) -these systems were excluded from analysis. While, in principle, CREST can generate many conformers per compound, we find that for the vast majority of compounds, only a few conformers are generated within 6 kcal/mol as calculated by GFN2 (Figure -S6), consistent with our previous work. In this work, the lowest-energy conformer by GFN2 energy was analyzed. Torsional angle SMILES arbitrary target specification (SMARTS) patterns from the experimental torsion knowledge-based distance geometry approach (ETKDG) were used to generate the histograms for the gas-phase data. ETKDG derives torsional preference distributions for the RDKit distance geometry coordinate generation method, from the analysis of experimental crystallographic data for a set of hierarchical dihedral patterns. These patterns are constructed from molecules with central bonds of C-C (168 acyclic patterns), C-O (56 acyclic patterns), C-S (16 acyclic patterns), N-C (131 acyclic patterns), N-S (4 acyclic patterns), and S-S (1 pattern) and create the 387 acyclic and 105 ring dihedral SMARTS patterns used to generate histograms of matching torsions with a stepsize of 5 • using RDKit Python scripts (see supporting information). Figures depicting the SMARTS patterns were generated using SMARTS.plus. For selected compounds, to compare the GFN2 geometry with density functional theory, optimization was performed with ORCA 4.2.0, 48 using the ωB97X-D functional 34 and def2-SVP basis set, which has proven to produce fairly accurate conformational energetics although some errors still exist when compared with more accurate methods.