More recently, machine learning (ML) approaches have proliferated that predict various proper-ties of reactions, including activation energy prediction directly from reactant and product graphs (i.e., without transition state localization) and TS geometry prediction. In a recently published study, a subset of the current authors benchmarked multiple state-of-the-art edge graph attention and message-passing neural networks on directly predicting reaction barriers from reaction graphs, eliminating the need for geometry optimizations or geometry predictions. These benchmarks showed that while such models reach irreducible errors on in-domain testing data, they perform poorly, and in some cases worse than random baselines, on out-of-domain barrier prediction tasks like unseen reaction mechanisms, larger molecularity, or a greater number of heavy atoms. On the other hand, data-driven approaches that predict TS geometries remain limited (for the time being) by reaction data, unreliable accuracy, and applicability to only very small molecules. An early example by Pattnaik et al. only targeted unimolecular isomerization reactions, required DFT optimization of reactant and product geometries, and was unreliable for reactions with a large number of possible conformations. More recent work by Duan et al. trained state-of-the-art models to predict TS geometries from reaction graphs and then from these calculate geometry-based activation barriers. One interesting finding was limited correlation in the accuracies of the predicted geometries and their barriers, meaning that an accurately predicted TS geometry does not guarantee an accurate barrier and vice versa. Additionally, both these works on TS geometry prediction have only been trained and tested on reactions with up to 7 heavy atoms and may not be reliable for larger molecules, limiting their real-world usability.