RMSD represents the square root of the mean of the squared deviations between predicted and reference geometries based on the Euclidean distance between corresponding atoms. For a system of 𝑛 atoms, the deviation is calculated directly from the Cartesian coordinates. Additionally, RMSD values are not normalised, which can disproportionately skew results when datasets include highly flexible molecules with many rotatable bonds. Torsion Fingerprint Deviations have been proposed as an alternative metric, which focus on torsional angles within an atomic system and aim to address some of the limitations associated with D-MAE and RMSD. This metric may offer a more robust approach for evaluating the performance of ML geometry prediction models. A more chemically meaningful alternative is to compare the calculated energies of the predicted and reference geometries, as this removes the dependence on system size. This approach is discussed further in Section 5.