This multifaceted nature makes MultiXC-QM9 useful for delta learning, transfer learning and multitask learning. The presence of data from both high and low fidelity methods enables models to learn the relationships between these methods and improve prediction accuracy. Computational Methodology: The MultiXC-QM9 data set was constructed using xyz molecular geometries obtained from the QM9-G4MP2 data set . Molecular energies were computed using the ADF package, which implements various post-self-consistent field (SCF) methods for different functionals. Specifically, the PBE functional was employed for generalized gradient approximation (GGA) level energy calculations, with SZ, DZP, and TZP basis sets; these calculations were performed for 133k molecules. Additionally, single-point energies were computed using the GFN2-xTB semi-empirical method.