2026-05-20 北海道大学
原子核は陽子と中性子が相互作用することで変形し、レモン型、ミカン型、洋ナシ型などの「形」を持つ。変形した原子核は振動、回転といった集団運動を起こす。AIの機械学習を組み込んだ原子核構造計算により、集団運動を予言することに成功した。
＜関連情報＞
- https://www.hokudai.ac.jp/news/2026/05/ai-10.html
- https://www.sciencedirect.com/science/article/pii/S0370269326003758
機械学習を用いた相互作用するボソン模型パラメータの微視的導出 Microscopic derivation of the interacting boson model parameters with machine learning
Y. Obata, K. Nomura
Physics Letters B Available online: 8 May 2026
DOI:https://doi.org/10.1016/j.physletb.2026.140522
Abstract
Machine learning is applied to derive microscopically parameters of the interacting boson model for nuclear spectroscopy. A physics-guided neural network is proposed, which is trained to map the potential energy landscapes that are calculated within the nuclear density functional theory onto the bosonic parameter space. To incorporate the underlying nuclear structure information and mitigate parameter degeneracy, the network integrates a global quadrupole collectivity indicator and valence nucleon numbers as key input features. In its applications to rare-earth nuclei, by reproducing the microscopic energy landscapes without any manual parameter tuning, the trained network is shown to provide a set of the model parameters and energy spectra that reflect the nuclear structural evolution, offering a robust alternative microscopic description of nuclear collectivity.
