Calculation of nuclear ground states up to A=6 using Artificial Neural Networks.

Nuclei are self-bound, many-fermion systems which exhibit genuinely quantum-mechanical properties. Understanding their structure and dynamics starting from the microscopic interactions among the constituent protons and neutrons is a formidable computational challenge, and solving the many-body Schrödinger equation beyond small nuclei necessarily involves approximations. In this talk, we present a scalable and novel solution of spin-dependent nuclear systems using Artificial Neural Networks to model ground state wavefunctions, reaching state of the art precisions on light systems and favorably scaling with the number of nucleons. We successfully benchmark nuclear binding energies, point-nucleon densities, and radii with the highly-accurate Green's function Monte Carlo and hyperspherical harmonics methods. The extensions of our approach to larger systems will also be discussed.