In this subsection, we explored the potential of using transfer learning to enhance the Chemprop model's performance. This approach involves initially training the model on a large dataset of activation energies computed with GFN2-xTB, followed by a second round of refining with the high-level CCSD(T)-F12a training dataset to improve the model. The GFN2-xTB reactions were derived from the Reaction Graph Depth 1 (RGD1) dataset, which contains approximately 168,000 organic reactions involving molecules with up to 10 heavy atoms. This low-level dataset encompasses a broad range of chemical reactions, and analysis using RMG database template identified 16 distinct reaction types, as shown in Figure . Notably, a significant portion of the reactions in this dataset do not match any RMG template, indicating that many reactions fall outside the scope of the RMG database.