BonDNet is a reaction-property graph neural network originally constructed for the prediction of reaction ∆G rxn values in single bond dissociation reactions. It consists of two modules, the graph-to-graph and graph-to-property modules, each constitute the processing of the original feature graph towards final prediction. The graph-to-graph module intakes the original knowledge graph G(B, A, g) and transforms it, via successive messagepassing steps, to the final graph G(B ′ , A ′ , g ′ ). Updates are per-formed on each separate reaction molecule prior to the construction of a global reaction difference graph. The reaction graph is constructed via the mapping of atoms and bonds in reactants to corresponding atoms in the products prior to a simple subtraction. The finalized reaction graph is embedded into a fixed-size vector via a global embedding set2set layer prior to feed-forward neural network layers for property prediction. Here QTAIM descriptors offer a promising avenue for highlighting nuanced changes in electronic structure between products and reactants, even at distal locations from the reaction site. We adapted our code to work natively with newer variants of the BonDNet architecture. This architecture was recently updated to improve generalizability for custom user descriptors and arbitrary reaction molecularity -essential quality of life updates that make it a prime model for testing an integrated QTAIM/ML approach . Furthermore, this updated architecture allows for custom bond definitions, thus, we integrate QTAIM bond path connectivities to define bonds within our molecular graphs.