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Approximate density functional theory (DFT) has become indispensable owing toits cost-accuracy trade-off in comparison to more computationally demanding butaccurate correlated wavefunction theory. To date, however, no single densityfunctional approximation (DFA) with universal accuracy has been identified,leading to uncertainty in the quality of data generated from DFT. With electrondensity fitting and transfer learning, we build a DFA recommender that selectsthe DFA with the lowest expected error with respect to gold standard butcost-prohibitive coupled cluster theory in a system-specific manner. Wedemonstrate this recommender approach on vertical spin-splitting energyevaluation for challenging transition metal complexes. Our recommender predictstop-performing DFAs and yields excellent accuracy (ca. 2 kcal/mol) for chemicaldiscovery, outperforming both individual transfer learning models and thesingle best functional in a set of 48 DFAs. We demonstrate the transferabilityof the DFA recommender to experimentally synthesized compounds with distinctchemistry.

A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery