Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Condensed-matter physics

Bridging the gap in electronic structure calculations via machine learning

Nature Computational Science volume 4, pages 729–730 (2024)Cite this article

Subjects

A highly efficient reconstruction method has been developed for the direct computation of Hamiltonian matrices in the atomic orbital basis from density functional theory calculations originally performed in the plane wave basis. This enables machine learning calculations of electronic structures on a large scale, which are otherwise not feasible with standard methods, and thus fills a methodological gap in terms of accessible length scales.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Illustration of the developed reconstruction method.

References

  1. Gong, X., Louie, S. G., Duan, W. & Xu, Y. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00701-9 (2024).

    Article  Google Scholar 

  2. Hohenberg, P. & Kohn, W. Phys. Rev. 136, B864 (1964).

    Article  Google Scholar 

  3. Pribram-Jones, A. et al. Annu. Rev. Phys. Chem. 66, 283 (2015).

    Article  Google Scholar 

  4. Kulik, H. J. et al. Electron. Struct. 4, 023004 (2022).

    Article  Google Scholar 

  5. Fiedler, L. et al. Phys. Rev. Mater. 6, 040301 (2022).

    Article  Google Scholar 

  6. Li, H. et al. Nat. Comput. Sci. 2, 367 (2022).

    Article  Google Scholar 

  7. Kohn, W. Phys. Rev. Lett. 76, 3168 (1996).

    Article  Google Scholar 

  8. Rackers, J. A. et al. Mach. Learn. Sci. Technol. 4, 015027 (2023).

    Article  Google Scholar 

  9. Fiedler, L. et al. npj Comput. Mater. 9, 115 (2023).

    Article  Google Scholar 

  10. Shao, X. et al. Nat. Commun. 14, 6281 (2023).

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the Center for Advanced Systems Understanding (CASUS), financed by Germany’s Federal Ministry of Education and Research (BMBF) and the Saxon state government out of the state budget approved by the Saxon State Parliament.

Author information

Authors and Affiliations

  1. Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf, Görlitz, Germany

    Attila Cangi

Authors
  1. Attila Cangi
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Attila Cangi.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cangi, A. Bridging the gap in electronic structure calculations via machine learning. Nat Comput Sci 4, 729–730 (2024). https://doi.org/10.1038/s43588-024-00707-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-024-00707-3

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing