Nuclear energy density functionals from machine learning

TL;DR

This paper introduces a novel machine learning approach to develop an orbital-free nuclear energy density functional, achieving high accuracy in predicting nuclear properties without relying on traditional Kohn-Sham equations.

Contribution

It presents the first machine learning-based orbital-free energy density functional for nuclei, significantly improving accuracy over existing methods.

Findings

Achieves high-accuracy predictions of ground-state densities and energies.

Bypasses Kohn-Sham equations with self-consistent calculations.

Outperforms existing orbital-free density functional theories for nuclei.

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.