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.

Abstract

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.

Keywords

Machine Learning
Pair-Density Functional Theory
Strongly Correlated
MC-PDFT
Data-Driven
High-Throughput

Supplementary materials

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Supporting Information
Description
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.
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