The significance of this work lies in its potential to bridge the gap between the current capabilities of GNN models and the practical requirements of chemical reaction engineering. By rigorously evaluating and optimizing these advanced methodologies, our study not only contributes to the theoretical understanding of kinetic parameter prediction but also offers practical guidelines for developing more accurate and efficient models. These advancements could accelerate the discovery and design of new materials and chemical processes, thereby having a profound impact on various scientific and industrial applications. Three data augmentation approaches were tested: (b) Transfer learning approach, where the model is pretrained on low-level SQM activation energies and fine-tuned with highlevel CCSD(T)-F12a data; (c) Delta learning approach, which either uses low-level activation energy as an additional feature or predicts the difference between low-and high-level activation energies; (d) Feature engineering approach, incorporating properties such as the heat of reaction, calculated from low-level data, as additional input features for the model.