Nuclear energy density functionals from machine learning

Abstract

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.

arXiv comment6 pages, 3 figures, 1 table

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Cited by in corpus (12)

• Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
• Prediction of Nuclear Charge Density Distribution with Feedback Neural Network
• Improved phenomenological nuclear charge radius formulae with kernel ridge regression
• Nuclear charge radius predictions by kernel ridge regression with odd-even effects
• Principal components of nuclear mass models
• Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional
• Constraining the Woods-Saxon potential in fusion reactions based on the neural network
• Optimization of generator coordinate method with machine-learning techniques for nuclear spectra and neutrinoless double-beta decay: ridge regression for nuclei with axial deformation
• Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
• Non-local orbital-free density functional theory incorporating nuclear shell effects
• Relativistic orbital-free kinetic energy density functional for one-particle nuclear systems
• Basis Representation for Nuclear Densities from Principal Component Analysis