We implemented several graph neural network architectures in our approach to ensure a wide-range of algorithms were benchmarked with/without QTAIM descriptors. These architectures included different update and pooling functions to ensure that relatively up-to-date models were compared. For update functions, we used traditional graph convolutions , graph attention mechanisms , and residual convolutions . These layers were selected for their diversity and ability to learn at different model depths with attention and residual connections typically being Pooling functions ultimately intake raw or processed graphs and compute a fixed-sized representation for visualization or tasks via a dense neural network. These layers are highly important and vary in complexity from simple sum operations to complex setTransformer architectures incorporating attention and beyond . Here we integrate 4 such operations into our potential space of graph neural networks: global summing, weighted global summing, set2set, and global attention pooling. These layers were selected to span a space of expressiveness and cost for our benchmarking and provide a wide toolkit for future QTAIM-enabled machine learning experiments. In order to merge QTAIM-features with nodes in our heterographs, we parsed Multiwfn's outputs to map features at NCP/BCPs to nodes based on attractors that aligned with atomic positions. For BCPs, Multiwfn also outputs attactors that terminate bond paths, which we parsed to their respective bonds. This avoided any non-nuclear attractors (NNAs) appearing as atom nodes in our graphs.