SOURCE-LINKED INTELLIGENCE

Learning Nuclear Structure with AI: Radii and Collectivity

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Low-energy nuclear structure is encoded in a broad body of experimental information across the chart of nuclides. Learning how this information is organized across observables and nuclei can provide a data-driven empirical baseline for theoretical extrapolations and experimental design. Here, we develop held-out ensembles based on NuCLR (Nuclear Co-Learned Representations), a multi-task model of nuclear data, to study charge radii and electric-quadrupole transition strengths. Out-of-fold (OOF) validation shows that shared representation improves performance over single-task learning, yielding

Read original source ↗ Open in workspace

recordType

paper

region

Global