83 citations · 176 across the 11 of their papers we have counts for
12 papers
Quantum convolutional neural network for predicting nuclear charge radii
Jinzhe Wu, Jianping Zhao, Tianshuai Shang +4
Quantum machine learning has the potential to become a new tool for understanding complex nuclear structures. In this work, we apply a hybrid quantum convolutional neural network (…
Microscopic Study of Charge Properties in Halo Nuclei
Yun Dong Wang, Hui Hui Xie, Tian Shuai Shang +3
Employing the relativistic continuum Hartree-Bogoliubov (RCHB) theory with intrinsic electromagnetic structure corrections, this work primarily investigates the charge properties o…
Systematic study of one-point kinetic energy density functionals for atomic nuclei
Tian Shuai Shang, Jian Li, Haozhao Liang +5
To explore the applicability of orbital-free density functional theory (OF-DFT) in nuclear physics, we perform a systematic benchmark of 36 one-point kinetic energy density functio…
Predictions of charge density distributions for nuclei with
Yun Dong Wang, Tian Shuai Shang, Hui Hui Xie +3
A deep neural network (DNN) has been developed to accurately predict nuclear charge density distributions for nuclei with proton numbers . By incorporating essential nucl…
Finite-nuclear-size effect for hydrogenlike ions under high external pressure
Dengshan Liu, Huihui Xie, Pengxiang Du +4
The influence of pressure on finite-nuclear-size corrections to atomic energy levels and electron-capture decay rate is investigated in confined hydrogenlike ions. The ions are mod…
Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning
Yanhua Lu, Tianshuai Shang, Pengxiang Du +2
Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexity. This study introduces the K…