Machine-Learned Functionals for Strongly Correlated Systems

22 June 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Machine Learning
Pair-Density Functional Theory
Strongly Correlated

Supplementary materials

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. Per- formance 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, cyclobu- tadiene, and 1,3-bis(methylene)-cyclobutadiene.


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.