most citedGlobal prediction of nuclear charge density distributions using deep neural network

22 citations · 50 across the 7 of their papers we have counts for

collaborators

7 papers

physics.atom-ph2026

The Pseudospectral Method for the Dirac Equation with Confining Potential

Dengshan Liu, Huihui Xie, Pengxiang Du +2

We observe that solving the Dirac equation for confined potentials using the generalized pseudospectral (GPS) method leads to deteriorating convergence of energy eigenvalues and hi…

nucl-th2026

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…

physics.atom-ph2026★ 1 cited

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…

quant-ph2024★ 6 cited

A deep neural network approach to solve the Dirac equation

Chuanxin Wang, Tomoya Naito, Jian Li +1

We extend the method from [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)] to solve the Dirac equation not only for the ground state but also for low-lying excit…

nucl-th2024★ 22 cited

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…

nucl-th2024★ 19 cited

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…

nucl-th2024★ 2 cited

A neural network approach for two-body systems with spin and isospin degrees of freedom

Chuanxin Wang, Tomoya Naito, Jian Li +1

We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Naito, Naito, and Hashimoto, Phys. Rev. Research…