Analysis of reaction networks is gaining popularity in many areas of science such as heterogeneous catalysis, 1 atmospheric chemistry, 2 and combustion modeling. Several geometry optimizations are needed to build such networks. Hence, cheap methods, such as low-level density functional theory (DFT) or semi-empirical methods, are used in the exploration stage. The use of lower-level methods can incur large errors that are difficult to anticipate. Thus, a computational model of reaction networks to predict chemical outcomes using low-level DFT-derived energies may lead to erroneous conclusions. This type of error in reaction energies can be reduced impromptu as shown by Christensen et al. In contrast, the choice of higher-level methods (e.g., hybrid or meta-hybrid DFT) would require high computational cost and is infeasible to use for large networks. Furthermore, accuracy is not always guaranteed even for higher-level DFT methods. Therefore, a simplistic approach to predicting energies with high accuracy and without much added computational cost would be game-changer in this field of research.