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:

Neural networks

Pushing the limits of OFDFT with neural networks

A neural network-based method for advancing orbital-free density functional theory (OFDFT) is developed, which reaches DFT accuracy and maintains lower cost complexity.

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

Access options

Buy this article

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

Fig. 1: Overview of approaches in orbital-free density functional theory.

References

  1. Keith, J. A. et al. Chem. Rev. 121, 9816–9872 (2021).

    Article  Google Scholar 

  2. Zhang, H. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00605-8 (2024).

    Article  Google Scholar 

  3. O’Malley, P. J. J. et al. Phys. Rev. X 6, 031007 (2016).

    Google Scholar 

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

    Article  Google Scholar 

  5. Kohn, W. & Sham, L. J. Phys. Rev. 140, A1133 (1965).

    Article  Google Scholar 

  6. Mi, W., Luo, K., Trickey, S. B. & Pavanello, M. Chem. Rev. 123, 12039–12104 (2023).

    Article  Google Scholar 

  7. Ying, C. et al. Adv. Neural Inf. Process. Syst. 34, 28877–28888 (2021).

    Google Scholar 

  8. Perdew, J. P., Burke, K. & Ernzerhof, M. Phys. Rev. Lett. 77, 3865 (1996).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andreas W. Hauser.

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

Hauser, A.W. Pushing the limits of OFDFT with neural networks. Nat Comput Sci 4, 163–164 (2024). https://doi.org/10.1038/s43588-024-00610-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-024-00610-x

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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