Unlike transfer learning and delta learning, which focus on low-level activation energy data, feature engineering involves using low-level calculations to compute additional properties that may be related to activation energy. These computed properties are then added as input features to enhance the reaction representation generated by the D-MPNN model. Among the various properties we considered (Table ), thermodynamic properties had the most significant impact on reducing errors in activation energy predictions, as shown in Table and Figure . This improvement can be attributed to the positive correlation between activation energy and reaction energy, as described by the Bell-Evans-Polanyi principle. By incorporating thermodynamic features, the model is better equipped to capture the underlying relationships that influence activation energy, leading to more accurate predictions.