A host of geometric learning algorithms were developed or adapted to interoperate with our QTAIM generation framework: molecular graph neural networks spanning graph convolutional networks (GCNs), residual convolutions, heterograph graph attention (GAT) layers, Chemprop (albeit only for molecular property predictions with atomic QTAIM features), and a variant of the BondNet architecture for reaction-level property predictions. Further details on each architecture implementation follow.