Neural-Network-Based Selective Configuration Interaction Approach to Molecular 
Electronic Structure

Schmerwitz, Yorick L. A.; Thirion, Louis; Levi, Gianluca; Jónsson, Elvar Ö.; Bilous, Pavlo; Jónsson, Hannes; Hansmann, Philipp

doi:10.1021/acs.jctc.4c01479

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Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure

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Schmerwitz, Yorick L. A.
Science Institute and Faculty of Physical Sciences, University of Iceland;
Research Department Neese, Max-Planck-Institut für Kohlenforschung, Max Planck Society;

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Citation

Schmerwitz, Y. L. A., Thirion, L., Levi, G., Jónsson, E. Ö., Bilous, P., Jónsson, H., et al. (2025). Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure. Journal of Chemical Theory and Computation, 21(5), 2301-2310. doi:10.1021/acs.jctc.4c01479.

Cite as: https://hdl.handle.net/21.11116/0000-0010-F266-A

Abstract

By combining Hartree–Fock with a neural-network-supported quantum-cluster solver proposed recently in the context of solid-state lattice models, we formulate a scheme for selective neural-network configuration interaction (NNCI) calculations and implement it with various options for the type of basis set and boundary conditions. The method’s performance is evaluated in studies of several small molecules as a step toward calculations of larger systems. In particular, the correlation energy in the N₂ molecule is compared with published full CI calculations that included nearly 10¹⁰ Slater determinants, and the results are reproduced with only 4 × 10⁵ determinants using NNCI. A clear advantage is seen from increasing the set of orbitals included rather than approaching full CI for a smaller set. The method’s high efficiency and implementation in a condensed matter simulation software expands the applicability of CI calculations to a wider range of problems, even extended systems through an embedding approach.