A recent study published in Nature outlines a multi-objective iterative symbolic regression framework that extracts analytical nuclear models using machine learning. By combining traditional models with uncertainty quantification, the research offers a refined prediction of nuclear binding energy and charge radii. This innovative approach invites further exploration in computational nuclear physics.