hand-selected features based on prior heuristics and thus, potentially missed leveraging many useful features. With our approach, we integrate a rich set of over 20 atom and 20 bond critical point features for an exhaustive toolkit of electronic descriptors (Table ). Integrating these features into graph neural networks (GNNs) allows for greater applicability to systems with varying chemical structures and unexplored chemical motifs where heuristics have not yet been developed. In addition, graphs are a flexible data structure that can readily intake spatial information such as atomic positions and/or bond lengths to further inform predictions. Given the power and ubiquity of geometric learning in chemical spaces, coupling them to chemicallyinformed features could extend their applicability and ability to generalize on smaller datasets . Notably, graph neural networks (GNNs) often perform poorly under low data regimes -regimes where experimental and high-accuracy quantum chemical calculations may operate and electronic descriptors could offer a strategy for bridging performance and small datasets. Furthermore, GNNs suffer from poor out-of-domain (OOD) extrapolation and we probe whether QTAIM features can help alleviate this shortcoming. We note one other study (that is not peerreviewed at the time of writing) that takes a somewhat similar approach to using QTAIM for geometric machine learning but ours differs in having benchmarks on standard datasets, testing on spin/charge-varying datasets, testing out-of-domain performance, and providing tools for generating and training QTAIMinformed geometric learning models for both molecules and reactions.