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 couples 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
novel deep-learning variational quantum Monte Carlo approach for \emph{ab
initio} nuclear structure. We show that FeynmanNet can provide very accurate
ground-state energies and wave functions for 4He, 6Li, and even up to
16O 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.Comment: 13 pages, 3 figure