The QM9S dataset, used for training and testing the DetaNet deep learning model, consists of 133,885 organic molecules derived from the QM9 dataset. The molecular geometries were re-optimized using the Gaussian 16 software at the B3LYP/def-TZVP level of theory. A wide range of molecular properties were then calculated at the same level, including scalar values (e.g., energy, partial charges), vectors (e.g., electric dipole), second-order tensors (e.g., Hessian matrix, quadrupole moment, polarizability), and third-order tensors (e.g., octupole moment, first hyperpolarizability). Additionally, frequency analysis and time-dependent DFT were performed to obtain the IR, Raman, and UV-Vis spectra.