QTAIM-enabled algorithms were pitted against a diverse set of molecular-graph property algorithms. Our aim here was not necessarily to outperform SOTA models but to demonstrate that models with QTAIM features could approach these models in performance and thus serve as the basis for more-advanced QTAIMenabled algorithms. Benchmarks on molecular properties were performed Schnet, PaiNN, and Chemprop. We briefly overview Schnet and PaiNN here. The Schnet architecture introduced the concept of continuous convolution filters. These convolution operations allow for the arbitrary position of atoms within the model representation and give SchNet improved performance over their direct legacy algorithms, DTNNs. PaiNN is an equivariant neural network architecture, it couples ideas from SchNet to new representations, enabling more data-efficient learning. Perhaps the biggest algorithmic development of PaiNN is the use of equivariant message passing functions that incorporate not only rotationally invariant distances but also rotationally equivariant neighbor directions as part of the message-passing update function. This allows the algorithm to predict tensorial properties, as well as generalize well with less data. Its efficient representations also allow for effective models with fewer parameters and shorter inference times. We note that our baseline GNN architectures are comparatively less sophisticated than many of these algorithms, and as such, we hope to bridge performance gaps with these models via the inclusion of QTAIM features alone. These models were benchmarked competitively on QM8 and QM9 as the remaining datasets required spin/charge information or covered reactionlevel properties.