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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.

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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 

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Correspondence to Andreas W. Hauser.

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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

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