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 Laboratory for Materials Simulations (LMS), Paul Scherrer Institute, 5352 Villigen, Switzerland

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

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

DOI10.24435/materialscloud:4s-xf [version v1]

Publication date: Jun 24, 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 2024.94 (2024), https://doi.org/10.24435/materialscloud:4s-xf

Description

Koopmans spectral functionals represent a powerful extension of Kohn-Sham density-functional theory (DFT), enabling accurate predictions 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 a manuscript of the same title, 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. This archive contains the data supporting the manuscript of the same title.

Materials Cloud sections using this data

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External references

Keywords

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