Supervised learning of a chemistry functional with damped dispersion

Kohn-Sham density functional theory is widely used in chemistry, but no functional can accurately predict the whole range of chemical properties, although recent progress by some doubly hybrid functionals comes close. Here, we optimized a singly hybrid functional called CF22D with higher across-the-board accuracy for chemistry than most of the existing non-doubly hybrid functionals by using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation that depends on density and occupied orbitals with a damped dispersion term that depends on geometry. We optimized this energy functional by using a large database and performance-triggered iterative supervised training. We combined several databases to create a very large, combined database whose use demonstrated the good performance of CF22D on barrier heights, isomerization energies, thermochemistry, noncovalent interactions, radical and nonradical chemistry, small and large systems, simple and complex systems and transition-metal chemistry.

Automated summary

In this study, we optimized a singly hybrid functional called CF22D, which outperforms most existing non-doubly hybrid functionals in terms of accuracy in chemistry. By using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation with a damped dispersion term, we trained CF22D using a large database and performance-triggered iterative supervised training. The use of a combined database demonstrated CF22D's good performance across various chemical properties and systems.