Reaching for the performance limit of hybrid density functional theory for molecular chemistry

TL;DR

This paper introduces a systematic protocol for developing more accurate and transferable density functionals in molecular chemistry, exemplified by the creation of the COACH functional within the RSH meta-GGA framework.

Contribution

The authors develop a new protocol combining constraint enforcement, flexible functional forms, and modern optimization, leading to the COACH functional with improved accuracy and transferability.

Findings

COACH outperforms leading RSH meta-GGAs on molecular benchmarks.

COACH maintains computational efficiency similar to existing methods.

Further progress may require incorporating nonlocal information.

Abstract

Density functional theory (DFT) offers an exceptional balance between accuracy and efficiency, but practical density functional approximations face an unavoidable trade-off among simplicity, accuracy, and transferability. A systematic protocol is therefore needed to develop functionals that are reliably most accurate within a chosen application domain. Here we present such a protocol by combining constraint enforcement, flexible functional forms, and modern optimization. Applying this strategy to the range-separated hybrid (RSH) meta-GGA framework, we obtain the carefully optimized and appropriately constrained hybrid (COACH) functional. Across broad molecular benchmarks, COACH improves both accuracy and transferability relative to leading RSH meta-GGAs, including \omegaB97M-V, while retaining the computational practicality of its rung. Finally, our analysis of the remaining trade-offs and saturation behavior suggests that further systematic progress will likely require the incorporation of genuinely nonlocal information.