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Machine learning nuclear orbital-free density functional based on Thomas–Fermi approach.
- Source :
-
International Journal of Modern Physics E: Nuclear Physics . Apr2024, Vol. 33 Issue 3/4, p1-9. 9p. - Publication Year :
- 2024
-
Abstract
- Orbital-free density functional theory (DFT) is much more efficient than the orbital-dependent Kohn–Sham DFT due to the avoidance of the auxiliary one-body orbitals. The machine learning approach has been applied to build nuclear orbital-free DFT recently [Wu et al., Phys. Rev. C 105 (2022) L031303] and achieved more precise descriptions for nuclei than existing orbital-free DFTs. Here, improved machine learning nuclear orbital-free density functional is built by including the Thomas–Fermi approach as a basement. Performances of the functional are compared in detail with the ones based on the pure machine learning approach. It is found that with the Thomas–Fermi functional included, the machine-learning-based functional can achieve better performance in directly predicting the kinetic energies and in providing the ground-state properties by the self-consistent procedures. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02183013
- Volume :
- 33
- Issue :
- 3/4
- Database :
- Academic Search Index
- Journal :
- International Journal of Modern Physics E: Nuclear Physics
- Publication Type :
- Academic Journal
- Accession number :
- 177204692
- Full Text :
- https://doi.org/10.1142/S0218301324500125