Understanding DFT Uncertainties for More Reliable Reactivity Predictions by Advancing the Analysis of Error Sources

Decades of advancements and thousands of successful applications
have contributed to the reliability of density functional theory (DFT)
methods. Especially in main group chemistry, DFT predictions tend
to be increasingly more reliable. In this study, we deeply analyze
unexpected (ca. 8–13 kcal/mol) DFT disagreements obtained for
a few organic reactions using only widely adopted, modern, hybrid,
and higher-rung DFT methods. To understand the underlying causes,
we move beyond conventional statistics-based benchmarks by combining
recent advances in DFT error decomposition with affordable gold-standard
references. This approach helps to characterize and disentangle multiple
functional and density-based error types and enables us to find functional(s)
suitable for broad mechanistic studies in all studied examples. The
proposed tools are cost-efficient, readily accessible, and easy to
integrate into routine thermochemistry workflows. While the focus
is on main group reactions, the approach is also applicable to transition
metal, bio-, and surface chemistry to assist more predictive reactivity
modeling.