In a rst attempt, we re-calculated the entire database using the desired functional. We expect this strategy to work well for functionals that yield a volume within 5% of the base functional (RPBE-D3), as the volume uctuations during training are typically 5%. For SCAN (very dierent melting temperature and density) and optPBE (signicantly larger density), we performed a complete on-the-y training. For the hybrid functional revPBE0, we rst upgraded a smaller on-the-y RPBE-D3 database containing 662 structures to revPBE0-D3/zd using ∆learning (database 2 in Ref. ), then continued training starting from this force eld and rening it using on-the-y training. This on-the-y training was done using the hybrid functional, an energy cuto of 1100 eV, and cooling liquid water twice from 350 K and 320 K, respectively, to 250 K, as well as melting cubic ice. The nal dataset comprises 1010 structures.