Improving coupled cluster theory for strongly correlated molecules with Kohn-Sham density encoding

Coupled cluster theory using a Kohn-Sham density functional reference can dramatically outperform the standard Hartree-Fock-based approach for strongly correlated systems, but the origin of these improvements has remained unclear. We show these improvements arise from differences in the one-particle density as encoded into the Fock matrix, not from the nature of the orbitals themselves, as is commonly assumed. Exploiting this insight, Kohn-Sham references enable near-chemical accuracy for electronic and thermochemical properties of transition metal dimers and main group compounds. Notably, Kohn-Sham coupled cluster qualitatively recovers the entire chromium dimer potential energy surface, a notorious failure case for conventional methods. We further introduce a density difference diagnostic that detects multireference character and guides the selection of optimal references at mean-field cost. Together, these results establish a practical route to treating strong correlation within quantum chemistry’s “gold standard” framework, with immediate implications for machine learning potential development and materials research, which rely heavily on density functional theory. Using density functional theory (DFT) densities in coupled-cluster theory dramatically improves its accuracy for strongly correlated transition-metal molecules, providing a simple way to generate beyond-DFT benchmark data for machine learning models.