However, unlike transfer learning, which utilizes GFN2-xTB activation energy data for model pretraining, delta learning directly incorporates the GFN2-xTB activation energy of a specific reaction to assist in predicting the corresponding CCSD(T)-F12a activation energy. This can be achieved in two ways: by including the GFN2-xTB activation energy as an additional input feature in the model, or by setting the model to predict the difference between the CCSD(T)-F12a and GFN2-xTB activation energies. The latter approach treats this difference as a correction factor to adjust the GFN2-xTB activation energy, thereby estimating the CCSD(T)-F12a activation energy. The performance of these two approaches is summarized in Table and Figure . Incorporating the GFN2-xTB activation energy as an additional input feature improves the model's accuracy in predicting the CCSD(T)-F12a activation energy, reducing the MAE from 6.16 to 3.97 kcal/mol. The approach of predicting the difference between the CCSD(T)-F12a and GFN2-xTB activation energies as a correction proves to be slightly more effective, further lowering the MAE to 3.85 kcal/mol.