Abstract
Understanding how protons and neutrons self-organize to form atomic nuclei requires solving the nuclear many-body Schr\"odinger equation. This work addresses this nontrivial task through a neural network quantum states ansatz that involves additional ``hidden'' degrees of freedom to improve the accuracy of the solution systemically. Light and medium-mass nuclei's energy and spatial density distributions are in excellent agreement with those obtained utilizing exact-diagonalization and diffusion Monte Carlo approaches.
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