  Approximate density functional theory (DFT) has become indispensable owing to
its cost-accuracy trade-off in comparison to more computationally demanding but
accurate correlated wavefunction theory. To date, however, no single density
functional approximation (DFA) with universal accuracy has been identified,
leading to uncertainty in the quality of data generated from DFT. With electron
density fitting and transfer learning, we build a DFA recommender that selects
the DFA with the lowest expected error with respect to gold standard but
cost-prohibitive coupled cluster theory in a system-specific manner. We
demonstrate this recommender approach on vertical spin-splitting energy
evaluation for challenging transition metal complexes. Our recommender predicts
top-performing DFAs and yields excellent accuracy (ca. 2 kcal/mol) for chemical
discovery, outperforming both individual transfer learning models and the
single best functional in a set of 48 DFAs. We demonstrate the transferability
of the DFA recommender to experimentally synthesized compounds with distinct
chemistry.
