Construction of Nuclear Covariant Energy Density Functional from A Physics-Guaranteed Neural Network Approach

Abstract

Density functional theory is a practical approach for solving quantum many-body problems with available computational resources. The complexity of the nuclear force makes constructing an accurate nuclear energy density functional much more challenging. The feasibility of constructing a nuclear covariant energy density functional with deep neural networks is demonstrated. This physics-guaranteed neural network approach achieves high accuracy in predicting nuclear energy density and exhibits significantly better extrapolation abilities than traditional machine learning methods for binding energies. When combined with the existing covariant density functional, the neural network approach improves the binding energy accuracy from $644$ keV to $86$ keV in the known region and also effectively captures the microscopic shell effect. Furthermore, its extrapolation performance is also significantly enhanced, achieving an accuracy of approximately $5$ MeV even when extrapolating up to $30$ steps. This work paves the way for the construction of accurate nuclear energy density functionals through machine learning.