Orbital-free density functionals based on real and reciprocal space separation
Abstract We introduce a general class of orbital-free density functionals (OF-DFT) decomposed into a local part in coordinate space and a local part in reciprocal space. As a demonstration of principle, we choose for the former the Thomas-Fermi-von Weizsäcker (TFW) kinetic energy density functional...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01643-0 |
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| Summary: | Abstract We introduce a general class of orbital-free density functionals (OF-DFT) decomposed into a local part in coordinate space and a local part in reciprocal space. As a demonstration of principle, we choose for the former the Thomas-Fermi-von Weizsäcker (TFW) kinetic energy density functional (KEDF) and for the latter a form derived from the Lindhard function, but with the two system-dependent adjustable parameters. These parameters are machine-learned from Kohn-Sham data using Bayesian linear regression with a kernel method, which employs moments of the Fourier components of the electronic density as the descriptor. Through a number of representative cases, we demonstrate that our machine-learned model provides more than an order-of-magnitude improvement in the accuracy of the frozen-phonon energies compared to the TFW KEDF, with negligible increase in the computational cost. Overall, this work opens an avenue for the construction of accurate KEDFs for OF-DFT. |
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| ISSN: | 2057-3960 |