Microscopic derivation of the interacting boson model parameters with machine learning

Nomura Kosuke

Department of Physics

May. 20, 2026

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。

Press release (in Japanese)

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, ……

Read the original article on Physics Letters B

Article inforamation
Y. Obata, K. Nomura, Microscopic derivation of the interacting boson model parameters with machine learning, Physics Letters B, Volume 878, 2026, 140522, ISSN 0370-2693,
https://doi.org/10.1016/j.physletb.2026.140522.