For the larger quantum datasets (QM9 and QMugs), we train and evaluate models for the HOMO and LUMO energy prediction tasks. For HOMO, on QM9 we notice decreases in MAE of 21.67% and 9.89% for SchNet and DimeNet++, respectively. On QMugs, the decreases are of 24.77% and 3.92% for SchNet and DimeNet++, respectively. Similar uplifts are observed for LUMO. Interestingly, total energy prediction on QMugs is improved by a large amount (58.97%) on SchNet, and by a moderate amount on DimeNet++ (19.34%), despite not being a local property like the HOMO or LUMO energies.