Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement
Maximally-localized Wannier functions (MLWFs) are widely employed as an essential tool for calculating the physical properties of materials due to their localized nature and computational efficiency. Projectability-disentangled Wannier functions (PDWFs) have recently emerged as a reliable and efficient approach for automatically constructing MLWFs that span both occupied and lowest unoccupied bands. Here, we extend the applicability of PDWFs to magnetic systems and/or those including spin-orbit coupling, and implement such extensions in automated workflows. Furthermore, we enhance the robustness and reliability of constructing PDWFs by defining an extended protocol that automatically expands the projectors manifold, when required, by introducing additional appropriate hydrogenic atomic orbitals. We benchmark our extended protocol on a set of 200 chemically diverse materials, as well as on the 40 systems with the largest band distance obtained with the standard PDWF approach, showing that on our test set the present approach delivers a success rate of over 98% in obtaining accurate Wannier-function interpolations, defined as an average band distance below 20 meV between the DFT and Wannier-interpolated bands, up to 2 eV above the Fermi level for metals or above the conduction band minimum for insulators (and a 100% success rate when including only bands up to 1 eV above these values).
Authors
- Nicola Marzari (ORCID: https://orcid.org/0000-0002-9764-0199)
- Giovanni Pizzi (ORCID: https://orcid.org/0000-0002-3583-4377)
- Yuhao Jiang (ORCID: https://orcid.org/0000-0002-3636-0230)
- Weisheng S. Zhao (ORCID: https://orcid.org/0000-0001-8088-0404)
- Junfeng Qiao
- Nataliya Paulish
Institutions
- Paul Scherrer Institute (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Beihang University (CN)
Publication Details
- Journal
- npj Computational Materials
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1038/s41524-025-01835-8
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- National Science Foundation
- Tencent
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- National Natural Science Foundation of China
- China Scholarship Council
- Staatssekretariat für Bildung, Forschung und Innovation
- National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials
- National Key Research and Development Program of China
- National Supercomputing Center, Korea Institute of Science and Technology Information