activity
20212024
most citedMachine learning the nuclear mass

119 citations · 177 across the 12 of their papers we have counts for

collaborators

12 papers

nucl-th2024

New insight into the N/Z and mass equilibration in heavy-ion collisions

Yu Yang, Zehong Liao, Zepeng Gao +3

The dynamics of N/Z and mass equilibration are investigated in the reactions 112,124Sn + 239Pu by employing the isospin-dependent quantum molecular dynamics model. It is found that…

nucl-th2024

Quantifying angular distributions in multinucleon transfer reactions with a semi-classical method

Zehong Liao, Zepeng Gao, Yu Yang +3

The multinucleon transfer (MNT) process in low-energy heavy ion collisions can be utilized to produce unknown nuclei far beyond the stability line. However, the reaction products e…

nucl-th2024

Bayesian uncertainty quantification for synthesizing superheavy elements

Yueping Fang, Zepeng Gao, Yinu Zhang +4

To improve the theoretical prediction power for synthesizing superheavy elements beyond Og, a Bayesian uncertainty quantification method is employed to evaluate the uncertainty of…

nucl-th2024

Shell effects on the drift and fluctuation in multinucleon transfer reactions

Zehong Liao, Zepeng Gao, Yu Yang +2

This study employs the dinuclear system model (DNS-sysu) to investigate drift and fluctuation mechanisms in 136Xe + 209Bi collisions above the Coulomb barrier. The DNS-sysu model d…

nucl-th2023

Role of the isospin diffusion on cluster transfer in C + Bi reactions

Zepeng Gao, Yinu Zhang, Long Zhu +4

Heavy-ion collisions at near-barrier energies provide a crucial pathway for investigating nucleon correlations and clustering structures. Recent experimental results showed that th…

nucl-th2023★ 1 cited

Importance of physical information on the prediction of heavy-ion fusion cross section with machine learning

Zhilong Li, Zepeng Gao, Ling Liu +3

In this work, the Light Gradient Boosting Machine (LightGBM), which is a modern decision tree based machine-learning algorithm, is used to study the fusion cross section (CS) of he…

nucl-th2023★ 15 cited

Constraining the Woods-Saxon potential in fusion reactions based on the neural network

Zepeng Gao, Siyu Liu, Peiwei Wen +5

The accurate determination of the nuclear interaction potential is essential for predicting the fusion cross sections and understanding the reaction mechanism, which plays an impor…

nucl-th2023

The optimal detection angles for producing N=126 neutron-rich isotones in the multinucleon transfer reactions

Zehong Liao, Long Zhu, Zepeng Gao +2

The challenge of isotopic identification over a wide angular distribution has limited the measurement of neutron-rich nuclei produced via the multinucleon transfer (MNT) process. T…

nucl-th2022★ 11 cited

Elliptic flow in heavy-ion collisions at intermediate energy: the role of impact parameter, mean field potential, and collision term

Bo Gao, Yongjia Wang, Zepeng Gao +1

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, by reverse tracing nucleons that are finally emitted at mid-rapidity (|| < 0.1) in the entire reaction p…

nucl-th2022

Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning

Yongjia Wang, Zepeng Gao, Hongliang Lü +1

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectat…

nucl-th2021★ 31 cited

Application of machine learning in the determination of impact parameter in the Sn+Sn system

Fupeng Li, Yongjia Wang, Zepeng Gao +5

Background: Sn+Sn collisions at the beam energy of 270 MeVnucleon have been performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the…

nucl-th2021★ 119 cited

Machine learning the nuclear mass

Zepeng Gao, Yongjia Wang, Hongliang Lü +3

Background: The masses of about 2500 nuclei have been measured experimentally, however more than 7000 isotopes are predicted to exist in the nuclear landscape from H (Z=1) to Og (Z…