Unlike transfer learning and delta learning, which focus on low-level activation energy data, feature engineering involves using low-level calculations to compute other properties that may be related to activation energy. As detailed in Table , we considered various properties related to thermodynamics, electronic structure, and hard and soft acids and bases (HSAB). For thermodynamic properties, we calculated these for both reactants and products, then determined the changes occurring during the reaction by subtracting the reactant properties from the product properties. These differences were used as additional inputs to the model. For electronic and HSAB properties, we followed the approach of Marques et al., computing the properties of the reactants and incorporating them as additional inputs to the model. Detailed calculation procedures are provided in the Supporting Information. These computed properties were then combined with the reaction representation generated by the GNN and subsequently fed into the FFNN model to improve the accuracy of activation energy predictions.