First, this approach trains the model to specifically identify and correct errors inherent in the GFN2-xTB calculations. By focusing on predicting the correction rather than directly predicting the activation energy, the learning task is simplified. These corrections tend to follow more consistent patterns, making them easier for the model to accurately learn. Furthermore, delta learning likely enhances generalization to unseen data, as the model is trained on residuals rather than the full energy values, which reduces the risk of overfitting-particularly when the dataset is limited in size or diversity. This improved generalization is critical for achieving reliable predictions across a wide range of reaction types. Collectively, these factors indicate that delta learning is a highly effective approach for enhancing the accuracy of activation energy predictions.