To mitigate this issue, we recalculated the CCSD(T)-F12a reactions using GFN2-xTB and included these data in the pretraining set alongside the RGD1 reactions. This approach allowed the model to encounter reactions from the target domain, albeit at a lower level of calculation, during the pretraining phase. This inclusion led to improved performance in predicting the activation energies of the CCSD(T)-F12a test set after fine-tuning (Table and Figure ). The results demonstrated a consistent improvement in both MAE and RMSE comparing with the baseline model, indicating that the effectiveness of the transfer learning approach is significantly influenced by the similarity between the pretraining dataset and the target task. Additionally, using reaction energy as the pretrained target (Table ) yielded lower predictive performance than activation energy, underscoring the challenges of transferring between different targets.