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Learning nuclear cross sections across the chart of nuclides with graph neural networks

Hongjun Choi1,*, Sinjini Mitra2, Jason Brodksy1, Ruben Glatt1, Erika Holmbeck1, Shusen Liu1, Nicolas Schunck1, Andre Sieverding1, and Kyle Wendt1

  • *Contact author: choi22@llnl.gov

Machine-learning the impact parameter in heavy-ion collisions at sNN=4 and 11 GeV: A cross-check study with UrQMD, AMPT, and JAM

Xiaoqing Yue, Guojun Wei, Yongjia Wang, Zhilong Li, Pengcheng Li, Haojie Xu, Xiangrong Zhu, Qingfeng Li, Fuhu Liu, and Yasushi Nara

Phys. Rev. C 114 014910 (2026)

Investigation of β -decay energy based on physically interpretable machine learning

S. J. Guo, S. Y. Wang, B. Qi, E. H. Wang, and Y. L. Huang

Phys. Rev. C 114 034332 (2026)

Study on intermediate- and high-energy proton- and neutron-induced fission cross-sections by coupling the Liège intranuclear cascade model with Bayesian neural networks*

Peiyan 培彦 Zhang 张, Zhiqiang 志强 Chen 陈, Rui 瑞 Han 韩, Bingyan 丙岩 Liu 刘, Hui 慧 Sun 孙, Guoyu 国玉 Tian 田, Xin 鑫 Zhang 张, and Qin 钦 Li 李

Chinese Phys. C 50 104108 (2026)

Artificial Neural Network–Based prediction of production cross sections of the medical radioisotopes 67Ga and 89Zr

Murat Okutan, Yiğit Ali Üncü, Gençay Sevim, Mert Şekerci, Tolga Kiliçarslan, Bayram Demir, and Hasan Özdoğan

Applied Radiation and Isotopes 230 112461 (2026)

Perspective: An outlook on fluorescence tracking

Lance W. Q. Xu and Steve Pressé

The Journal of Chemical Physics 163 160901 (2025)

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