To investigate the impact of QM descriptors on D-MPNN performance, we evaluate the performance of the QM-augmented D-MPNN on 16 public datasets. This includes classification and regression tasks on datasets spanning various fields such as quantum mechanics, physical chemistry, biophysics, and physiology. The dataset sizes vary from several hundred to one hundred thousand data points. Detailed descriptions of each dataset are listed in Table . It is important to note that the number of data points in some datasets does not match the original numbers from Wu et al., due to the existence of duplicated molecules, failed processing by RDKit, and failed SMILES conversion, as detailed in Yang et al. Additionally, we have fewer molecules in the ESOL and FreeSolv datasets because we additionally calculate QM descriptors for these species as part of our analysis, and some of these fail in the calculations.