Yannick Schubert¹, Sandra Luber¹, Nicola Marzari^2,3, Edward Linscott^3,4*

1 Department of Chemistry, University of Zurich, 8057 Zurich, Switzerland

2 Theory and Simulations of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

3 PSI Center for Scientific Computing, Theory and Data, 5232 Villigen PSI, Switzerland

4 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Paul Scherrer Institute PSI, 5352 Villigen PSI, Switzerland

* Corresponding authors emails: edward.linscott@psi.ch

DOI10.24435/materialscloud:w1-ev [version v2]

Publication date: Nov 14, 2024

How to cite this record

Yannick Schubert, Sandra Luber, Nicola Marzari, Edward Linscott, Predicting electronic screening for fast Koopmans spectral functional calculations, Materials Cloud Archive 2024.182 (2024), https://doi.org/10.24435/materialscloud:w1-ev

Description

Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enable the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that — with minimal training — can predict these screening parameters directly from orbital densities calculated at the DFT level. We show on two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run-times with minimal loss of accuracy, it will enable the application of Koopmans spectral functionals to classes of problems that previously would have been prohibitively expensive, such as the prediction of temperature-dependent spectral properties. More broadly, this work demonstrates that measuring violations of piecewise linearity (i.e. curvature in total energies with respect to occupancies) can be done efficiently by combining frozen-orbital approximations and machine learning.

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Keywords

Koopmans spectral functionals machine learning electronic structure orbital-density dependent functionals MARVEL/P4 SNSF