In recent years, remarkable progress has been made in combining machine learning with nuclear models. Some of these studies have a significant impact on physics at RIBF: The density functional theory and machine learning have been combined to predict the mass and lifetime of r-process elements, and the nuclear optical model and machine learning are combined to predict neutron-nucleus scattering. Thus, the combination of machine learning and nuclear modeling has great potential.
This mini-workshop will bring together the researchers to discuss how ML can predict physical quantities (mass, lifetime, and/or (n,γ) cross-section) related to the r-process more accurately and what experiments should be proposed to improve the prediction accuracy. We will also explore new opportunities for collaboration.