Jun 13 – 17, 2022
Berkeley, CA
US/Pacific timezone

Nuclear energy density functionals from machine learning

Not scheduled
Berkeley, CA

Berkeley, CA

Lawrence Berkeley National Laboratory
Oral Poster Presentations Poster Session


Xin-Hui Wu (Peking University)


Machine learning is employed to build an energy density functional for self-bound nuclear systems for the first time. By learning the kinetic energy as a functional of the nucleon density alone, a robust and accurate orbital-free density functional for nuclei is established. Self-consistent calculations that bypass the Kohn-Sham equations provide the ground-state densities, total energies, and root-mean-square radii with a high accuracy in comparison with the Kohn-Sham solutions. No existing orbital-free density functional theory comes close to this performance for nuclei. Therefore, it provides a new promising way for future developments of nuclear energy density functionals for the whole nuclear chart.

Primary authors

Xin-Hui Wu (Peking University) Dr Pengwei Zhao (Peking University) Dr Zhengxue Ren

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