The second approach we examined was delta learning. As illustrated in Figure , delta learning differs from transfer learning in that it directly uses low-level activation energy data to predict the target high-level activation energies. This can be achieved either by incorporating the low-level activation energy as an additional input feature in the FFNN or by setting the model to predict the difference between the low-and high-level activation energies. This approach allows the low-level activation energies to be corrected by the model's predictions, resulting in more accurate high-level activation energy estimates.