The first approach we investigated was transfer learning. As illustrated in Figure , this method involves initially training the D-MPNN model on a large dataset of activation energies computed with SQM methods. This pretraining phase allows the model to learn fundamental relationships between chemical structures and their associated activation energies from a substantial volume of low-level data. After this pretraining, the model is then fine-tuned using high-level CCSD(T)-F12a data to improve its predictive accuracy.