Deep-neural-network approach to solving the ab initio nuclear structure problem

TL;DR

This paper introduces FeynmanNet, a deep learning variational quantum Monte Carlo method that accurately predicts nuclear ground-state energies and wave functions for various nuclei, overcoming traditional fermion-sign problems.

Contribution

The paper presents a novel neural network-based approach for ab initio nuclear structure calculations that scales polynomially and improves accuracy over traditional methods.

Findings

Accurately predicts energies for nuclei up to 16O

Scales polynomially with nucleon number

Outperforms diffusion Monte Carlo in accuracy

Abstract

Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei it is still quite challenging because of the complicated nucleon-nucleon interactions, which strongly couple the spatial, spin, and isospin degrees of freedom. By combining essential physics of the nuclear wave functions and the strong expressive power of artificial neural networks, we develop FeynmanNet, a deep-learning variational quantum Monte Carlo approach for \emph{ab initio} nuclear structure. We show that FeynmanNet can provide very accurate solutions of ground-state energies and wave functions for $^4$He, $^6$Li, and even up to $^{16}$O as emerging from the leading-order and next-to-leading-order Hamiltonians of pionless effective field theory. Compared to the conventional diffusion Monte Carlo approaches, which suffer from the severe inherent fermion-sign problem, FeynmanNet reaches such a high accuracy in a variational way and scales polynomially with the number of nucleons. Therefore, it paves the way to a highly accurate and efficient \emph{ab initio} method for predicting nuclear properties based on the realistic interactions between nucleons.