The delta learning approaches exhibit the narrowest error distributions compared to both the baseline model and the other data augmentation strategies. To further evaluate these methods, we selected the model with the lowest MAE from each method (best values in Tables ) and summarized the results in Table . The results clearly indicate that delta learning is the most effective strategy for utilizing low-level data to enhance model accuracy in predicting activation energies. This trend remains robust even when alternative machine learning architectures were applied for activation energy prediction, as detailed in Table of the Supporting Information. However, it is important to acknowledge that delta learning also imposes higher computational demands compared to the other methods.