For QM9 stratification, there is a significant decline in model performance between both QTAIM and non-QTAIM models. Despite this, QTAIM-informed models demonstrate a moderate ability to generalize to much larger molecules despite being trained entirely on small molecules. We also note that the filtering of the QM9 dataset to only molecules with fewer than 13 atoms results in a training set of only 4,000 molecules. This comparatively small (2 orders of magnitude smaller than the full QM9 test set) training set also shows how QTAIM could be an effective tool for leveraging smaller datasets. We note the systematic overprediction of THE LUMO/gap energies and underprediction of THE HOMO energies in the QTAIM informed model, ). LIBE OOD tests also show a marked drop in testing performance, though not to the extent of the QM9 OOD test (Table ). The QTAIM model here remains quite serviceable while the model without QTAIM features is drastically worse versus in-domain testing. Changes in performance can be partially attributed to the reduction in training data (only 5,200 molecules in training). This notion is somewhat qualified by our learning tests (Figure ) where non-QTAIM models had better test errors (<125 meV/Atom) with only 1,000 training examples. Notably, both models exhibited a trend of overpredicting for positively charged molecules and underpredicting for those with a -1 charge, yet this deviation was less pronounced in QTAIMinformed models where a greater portion of test examples were within chemical accuracy (2.88% vs. 1.35%) (Fig. ). These results show that QTAIM can be an effective method for improving model robustness in out-of-domain experiments, especially in the context of charged species.