Machine learning (ML) is driving a paradigm shift across scientific disciplines, including quantum chemistry (QC), where it reshapes the landscape of used methods . The recent surge in ML has made Kohn-Sham Density Functional Theory (KS DFT), being already a cornerstone in quantum simulations for material science and chemistry, even more important (see recent review by von Lilienfeld and co-workers ). On one hand, DFT generates vast amounts of data to train ML models , greatly extending DFT's reach in time and length scales . On the other hand, ML techniques provide improved density functional approximations (DFAs), with the DM21 functional by DeepMind 37 being a prominent example.