Predicting electronic screening for fast Koopmans spectral functional calculations
Abstract Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables 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, orb...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01484-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850253850256080896 |
|---|---|
| author | Yannick Schubert Sandra Luber Nicola Marzari Edward Linscott |
| author_facet | Yannick Schubert Sandra Luber Nicola Marzari Edward Linscott |
| author_sort | Yannick Schubert |
| collection | DOAJ |
| description | Abstract Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables 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 in 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. |
| format | Article |
| id | doaj-art-ac2136edb4544a3cbc13fe2df8744f3d |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-ac2136edb4544a3cbc13fe2df8744f3d2025-08-20T01:57:16ZengNature Portfolionpj Computational Materials2057-39602024-12-0110111210.1038/s41524-024-01484-3Predicting electronic screening for fast Koopmans spectral functional calculationsYannick Schubert0Sandra Luber1Nicola Marzari2Edward Linscott3Department of Chemistry, University of ZurichDepartment of Chemistry, University of ZurichTheory and Simulations of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de LausanneCenter for Scientific Computing, Theory and Data, Paul Scherrer InstituteAbstract Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables 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 in 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.https://doi.org/10.1038/s41524-024-01484-3 |
| spellingShingle | Yannick Schubert Sandra Luber Nicola Marzari Edward Linscott Predicting electronic screening for fast Koopmans spectral functional calculations npj Computational Materials |
| title | Predicting electronic screening for fast Koopmans spectral functional calculations |
| title_full | Predicting electronic screening for fast Koopmans spectral functional calculations |
| title_fullStr | Predicting electronic screening for fast Koopmans spectral functional calculations |
| title_full_unstemmed | Predicting electronic screening for fast Koopmans spectral functional calculations |
| title_short | Predicting electronic screening for fast Koopmans spectral functional calculations |
| title_sort | predicting electronic screening for fast koopmans spectral functional calculations |
| url | https://doi.org/10.1038/s41524-024-01484-3 |
| work_keys_str_mv | AT yannickschubert predictingelectronicscreeningforfastkoopmansspectralfunctionalcalculations AT sandraluber predictingelectronicscreeningforfastkoopmansspectralfunctionalcalculations AT nicolamarzari predictingelectronicscreeningforfastkoopmansspectralfunctionalcalculations AT edwardlinscott predictingelectronicscreeningforfastkoopmansspectralfunctionalcalculations |