Learning Nuclear Structure with AI: Radii and Collectivity

Abstract: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 a charge-radius $\mathrm{RMS}$ deviation of $0.0147~{\rm fm}$ and a $\mathrm{B(E2)}$ $\mathrm{RMS}$ deviation of $0.192~e^2{\rm b}^2$ across hundreds of nuclides, competitive with state-of-the-art nuclear models. Our error bars estimate the expected prediction accuracy across the nuclear chart, highlighting regions where new data would encode information beyond the learned patterns. NuCLR thus serves as a data-driven surveyor of nuclear structure and a step toward a shared, multi-observable foundation model of the nuclear chart.

Nuclear Theory (nucl-th); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Nuclear Experiment (nucl-ex)

arXiv:2609.17838 [nucl-th]

(or arXiv:2609.17838v1 [nucl-th] for this version)

https://doi.org/10.48550/arXiv.2609.17838 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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