To evaluate the performance of different data augmentation approaches, we selected a diverse and high-quality dataset of reaction activation energies published by Spiekermann et al. This dataset originates from the work of Grambow et al., where reactions were calculated at the ωB97X-D3/def2-TZVP level of theory. The molecules involved in these reactions were selected from the GBD-7 database and are composed of H, C, N, and O atoms, with up to seven heavy atoms per molecule. The atom-mapped SMILES for these reactions were cleaned and updated using RDKit. In the study by Spiekermann et al., these reactions were reoptimized at the ωB97X-D3/def2-TZVP level, with single-point energies refined at the CCSD(T)-F12a/cc-pVDZ-F12 level of theory. The original CCSD(T)-F12a dataset comprises 11,927 reactions. However, since both transfer learning and delta learning require low-level SQM activation energies, as discussed below, we excluded reactions that failed to converge using the low-level methods. After removing these non-converging reactions, the final high-level CCSD(T)-F12a dataset used in this work consisted of 11,862 reactions. For the preparation of our training, validation, and testing datasets, we employed an 80:10:10 scaffold split on the CCSD(T)-F12a dataset based on the SMILES strings. This partitioning method utilizes the Bemis-Murcko scaffold, as scaffold splits provide a more robust assessment of model generalizability compared to random splits.