To obtain the nal MLPs for hybrid functionals, baseline RPBE-D3 calculations were conducted at 2000 eV. Subsequently, ∆-learning was employed to "upgrade" this dataset to dierent hybrid functionals. This was achieved by adding to the baseline database the energy, force and stress dierences between the desired hybrid functional and the RPBE-D3 functional at 1100 eV. To reduce errors, all structures generated with the hybrid functional were used in the database for ∆-learning. However, in independent tests, we found that using 5 and 64 structures resulted in test set errors of 6 meV/ Å and 2.5 meV/ Å for the dierence between two functionals. This does not add a signicant error to the baseline error of about 26 meV/ Å (error propagation follows the Gaussian law). So the ∆-learning approach taken here is on the safe side and does not introduce a noteworthy error. Indeed, comparison with a test set calculated at 2000 eV using the hybrid functional gives a force error of 28 meV/ Å, which is only slightly larger than the errors we found for semi-local density functionals (26 meV/ Å). The nal database used for hybrid functionals covers a much larger set of volumes than the original database and we term it "hybrid".