83 citations · 176 across the 11 of their papers we have counts for
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Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum, III: nuclei with
DRHBc Mass Table Collaboration, Peng Guo, Xiaojie Cao +90
The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for nuclei with $8 \leq Z \leq 12…
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…
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…
Prediction of Nuclear Charge Density Distribution with Feedback Neural Network
Tian-Shuai Shang, Jian Li, Zhong-Ming Niu
The nuclear charge density distribution plays an important role in nuclear physics and atomic physics. As one of the most frequently used models to obtain charge density distributi…
Global prediction of nuclear charge density distributions using deep neural network
Tian Shuai Shang, Hui Hui Xie, Jian Li +1
A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory…
Nuclear mass predictions based on convolutional neural network
Yanhua Lu, Tianshuai Shang, Pengxiang Du +3
A convolutional neural network (CNN) is employed to investigate nuclear mass. By introducing the masses of neighboring nuclei and the paring effects at the input layer of the netwo…
Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum, II: Even- nuclei
DRHBc Mass Table Collaboration, Peng Guo, Xiaojie Cao +80
The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even- nuclei with $8\le Z\…
Inference of Parameters for Back-shifted Fermi Gas Model using Feedback Neural Network
Peng-Xiang Du, Tian-Shuai Shang, Kun-Peng Geng +2
The back-shifted Fermi gas model is widely employed for calculating nuclear level density (NLD) as it can effectively reproduce experimental data by adjusting parameters. However,…
Pseudospin Symmetry: Microscopic origin of the ground-state inversion in neutron-rich odd-A Cu isotopes
Tianshuai Shang, Jian Li, Qiang Zhao
The role of the pseudospin symmetry in the inversion of the ground-state spin in Cu isotopes is investigated within the covariant density functional theory (CDFT). The density func…