Perhaps the most widely-adopted dataset for structure-toproperty benchmarking, QM9 is a dataset of optimized, small organic compounds consisting of 134,000 structures. These structures are limited to up to 9 heavy (CONF) atoms and up to 29 atoms including H. We constructed a train-test split of 90/10 and the validation set was constructed from the training set with a split of 80/20 for model selection and hyperparameter tuning. We benchmarked on 3 of the reported properties in the dataset, namely the energy of highest occupied molecular orbital (ε HOMO ), energy of lowest unoccupied molecular orbital (ε LUMO ), and the HOMO-LUMO gap (∆ ε ). We used this limited set as it included only size-intensive properties. Algorithms were trained in a multitask fashion to predict all three properties. QTAIM properties for this dataset were computed at TPSS 33 /def2-SVP with D3BJ dispersion. Here we aimed to study the efficacy of QTAIM features at lower levels of theory, given the comparatively low level of theory and cost of computed target values.