Despite its higher computational cost, delta learning offers distinct advantages. By using low-level activation energies for both training and querying, it simplifies the model's task to learning corrections between low-and high-level activation energies. This focused approach reduces the dependence on high-level data, which is often expensive and time-consuming to produce. To assess the effectiveness of delta learning in reducing the demand for high-fidelity data, we evaluated model performance when trained on varying fractions (10%-100%) of the original CCSD(T)-F12a dataset. As shown in Figure