The Chemprop and EGAT models used in this paper were trained on the RGD1 dataset, which contains around 177,000 reactions with up to ten heavy atoms consisting of carbon, hydrogen, nitrogen and oxygen. In brief, the RGD1 dataset was generated by a graph-based enumeration of ∼700,000 reactions involving reactants sampled from PubChem. A reaction conformational sampling strategy was applied to generate up to three conformations for each reaction that were used to initialize double-ended TS searches, followed by Berny optimization, and intrinsic reaction coordinate 67 (IRC) validation at the GFN2-xTB 68 level of theory. The GFN2-xTB optimized TSs were further refined using the Gaussian16 69 quantum chemistry engine at the B3LYP-D3/TZVP 70,71 level of theory with D3 dispersion. The DFT-level TS were classified as intended or unintended using an XGBoost 73 model that uses geometric features of the TS and the GFN2-xTB level information to classify the TS as intended or unintended (i.e., whether the TS corresponds to the reaction that was used to seed the TS search). This model exhibits a testing set accuracy of ∼ 95% in a previous smaller testing set. For a detailed description of the RGD1 database, we direct readers to our previous publication. Several data processing steps were applied to prepare the RGD1 data for training the activation energy models. Firstly, reactions were pruned by the uniqueness of the reactant and product