A remaining critical problem with ML in QC is their limited transferability-the ability to generalize to unseen data . These limitations hinder the applicability of ML models in QC, making users cautious and often leading them to stick with well-established old-school methods over new-school ML counterparts. In DFT, this situation has lead to a no man's land between oldschool and new-school DFAs : the promised revolution of ML based DFAs is hampered by the far broader applicability of old-school DFAs , such as B3LYP or PBE . For example, while DM21's training on systems with fractional charges and spin addresses some limitations of old-school functionals , its catastrophic transferability failures have recently become evident . Namely, it has been shown that DM21 does not converge for certain transition metal (TM) atoms-a convergence task easily handled by reputable old-school functionals . Thus, it is no wonder that organic chemists still prefer a) stefan.vuckovic@unifr.ch old-school DFAs over DM21 or other new-school models for TM-catalyzed reaction mechanisms , despite major TM shortcomings of the former .