Machine learning orbital-free density functional theory: taming quantum shell effects in deformed nuclei

• URL: http://arxiv.org/abs/2412.20739v1
• Date: Mon, 30 Dec 2024 06:23:34 GMT
• Title: Machine learning orbital-free density functional theory: taming quantum shell effects in deformed nuclei
• Authors: X. H. Wu, Z. X. Ren, P. W. Zhao,
• Abstract summary: We develop a machine learning approach to the orbital-free density functional theory. We describe the ground-state properties and potential energy curves for both spherical $16$O and deformed $20$Ne nuclei. This is the inaugural instance where a fully orbital-free energy density functional has succeeded in taming the complex shell effects in deformed nuclei.
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• Abstract: Accurate description of deformed atomic nuclei by the orbital-free density functional theory has been a longstanding textbook challenge, due to the difficulty in accounting for the intricate quantum shell effects that are present in such systems. Orbital-free density functional theory is, in principle, capable of describing all effects of nuclear systems, as guaranteed by the Hohenberg-Kohn theorem. However, from a microscopic perspective, shell and deformation effects are believed to be intrinsically connected to single-orbital structures, posing a significant challenge for orbital-free approaches. Here, we develop a machine learning approach to the orbital-free density functional theory, which is capable of achieving a high level of accuracy in describing the ground-state properties and potential energy curves for both spherical $^{16}$O and deformed $^{20}$Ne nuclei. This is the inaugural instance where a fully orbital-free energy density functional has succeeded in taming the complex shell effects in deformed nuclei. It demonstrates that the orbital-free energy density functional, which is directly based on the Hohenberg-Kohn theorem, is not only a theoretical concept but also a practical one for nuclear systems.

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