Skip to main content
Log in

Neural networks

Pushing the limits of OFDFT with neural networks

  • News & Views
  • Published:

From Nature Computational Science

View current issue Submit your manuscript

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, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

  • Springer Nature America, Inc.

Navigation