Computational Methodology: The data set is the revision of the QM9 data set with the properties of ca. 130k equilibrium geometries (with up to 9 nonhydrogen atoms) calculated at a more accurate aPBE0 level with the cc-pVTZ basis set. PySCF 2.4.0 was used to perform simulations, while the adaptive parameters were supplied to it by a machine learning model trained on so that aPBE targets the CCSD(T)/cc-pVTZ atomization energies. The code to make predictions with aPBE0 is available on GitHub. The properties calculated at aPBE/cc-pVTZ include total and atomization energies, orbital energies, dipole moments, and density matrices.