f values, they are often constrained by cost, time, and the limited scope of molecules that can be feasibly synthesized, isolated, and measured. In contrast, quantum mechanical (QM) simulations enable computation of ∆H • f for a much wider range of chemical species, including reactive intermediates and complex species inaccessible to conventional experiments. However, the accuracy of QM-based ∆H • f depends heavily on the chosen level of theory and the application of appropriate post-processing corrections to mitigate systematic errors. Moreover, the computational cost of QM methods can escalate steeply with molecular size and complexity. Although more expensive QM methods often produce better results, this trend does not strictly hold for all chemical spaces and levels of theory. Therefore, identifying an optimal method that balances accuracy requirements and resource constraints for a given molecular target is essential for efficient and reliable thermochemical modeling.