Torsion drive datasets were generated by enumerating all the torsions in molecules from Generation 2 optimization datasets and picking select molecules for a one-dimensional torsion scan. These torsions were chosen by listing all torsion definitions applied to each rotatable central bond in each molecule. This list of molecules was filtered, with each torsion scan ideally scanning a torsion exercising a single torsion in the force field. However, this was not always possible given the set of available molecules, so when no suitable molecules could be found, the number of allowed overlaps with other torsions was incremented by one and the process repeated until qualifying molecules were found. For the 1-dimensional torsional scans, the dihedral angle was sampled on a grid of 24 points spanning the range [-180, 180] with a spacing of 15 • , and the torsion potential scans were performed using the TorsionDrive package. For some of the molecules with in-ring torsions only a subset of the 24 grid points were retained as a full rotation takes the system into unphysically high energy regions and distorts the torsion drive. This energy cutoff on the grid was 0.05 Hartree (∼ 31.4 kcal/mol). TorsionDrive uses wavefront propagation to find the minimum energy conformation at each torsion angle along a torsion scan. The QM datasets described above were generated using the B3LYP-D3BJ/DZVP level of QM theory, the same level of theory used to generate training data for Parsley. The choice of QM theory level at which the training data is generated should be sufficiently accurate for metrics such as conformer energies and torsion profile energetics for a wide range of molecules. This choice of QM theory level includes the choice of functional as well as the basis set. Prior benchmarks using this theory level showed optimal performance on conformer energetics of the MPCONF196 dataset, which contains small peptides and medium-sized macrocycles, and on the YMPJ dataset, which is a dataset of natural amino acids. We have also conducted our own work benchmarking levels of theory for this data and reached a similar conclusion, which will be reported in a separate study.