Machine-Learned Energy Functionals for Strongly Correlated Systems


We introduce multiconfiguration data-driven functional theory (MC-DDFT) as a new approach to multiconfiguration nonclassical functional theory (MC-NCFT), in which the classical energy of a multiconfigurational wave function is combined with a machine-learned functional for the nonclassical exchange-correlation energy. We also present results obtained by a related approach, multiconfiguration energy-correcting functional theory (MC-ECFT), in which the total energy of a wave function method (e.g. CASSCF or NEVPT2) is corrected with a machine-learned functional. On a dataset of carbene singlet-triplet energy splittings, we demonstrate that these new multiconfiguration data-driven functional methods (MC-DDFMs) are able to achieve near-benchmark performance on systems not used for training while being less active-space dependent than multiconfiguration pair-density functional theory using currently available translated functionals.

Version notes

Added middle initial of Donald Truhlar (Donald G. Truhlar)


Supplementary material

Supporting Information
Names of the carbenes included in the training, validation, test, and test subsets. Histograms showing systematic errors in reference methods. Basis set dependence of MC-DDFMs. Performance and active space and basis set dependence of MC-DDFMs trained solely on density features or solely on on-top density features. Individual performances on benzene, cyclobutadiene, and 1,3-bis(methylene)-cyclobutadiene.