The accuracy of density functional approximations (DFAs) has become a limiting factor in scientific discoveries driven by electronic structure calculations and empowered by artificial intelligence . At the same time, the development of DFAs is currently in "no man's land". On the one hand, machine-learned DFAs hold promise to overcome the known deficiencies of human-designed functionals . Yet, their transferability remains a major challenge, essential for the broad applicability seen in their human-designed counterparts, such as PBE or B3LYP .