The energy errors from DFT methods have been well-studied in past decades . For example, Redfern et al. have shown that the errors from DFT for the formation energy of simple alkanes increase with the number of bonds. Grimme studied the limitation of the common DFT functionals to calculate formation enthalpies and reaction enthalpies of simple alkanes. Many improved DFT functionals are developed based on these types of benchmark studies to achieve higher accuracy. The M06 functionals are developed to reduce the errors generated from DFT and are highly parameterized. The Bayesian error estimation framework (BEEF) to DFT is another class that is used to study surface catalysis. The Gn class of composite methods very accurately predicts the energies of a much more diverse class of molecules. However, the trade-off between time and accuracy persists despite the development of many advanced DFT methods spanning multiple decades.