Variational Monte Carlo calculations of atomic nuclei with an artificial neural-network correlator ansatz

Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems, even when non-perturbative interactions are prominent. We introduce a neural-network quantum state ansatz suitable for modeling the ground-state wave function of light nuclei and approximately solving the nuclear many-body Schrödinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field. We will present results for the binding energies and point-nucleon densities of light nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parameterizations based on two- and three-body Jastrow functions and virtually-exact Green's function Monte Carlo results.