Theoretical and Computational Chemistry

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)


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Supplementary material

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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.