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 and …

In this study, we developed a neural network that incorporates a fully connected layer with a
convolutional layer to predict the nuclear charge radii based on the relationships between …

Neutron-induced fission is an important research object in basic science. Moreover, its
product yield data are an indispensable nuclear data basis in nuclear engineering and …

A machine-learning algorithm, Light Gradient Boosting Machine, was applied for the first
time to investigate the fundamental experimental observables in even-even nuclei over the …

The charge radius is a fundamental physical quantity that describes the size of one nucleus,
but contains rich information about the nuclear structure. There are already many machine …

Abstract Information on the decay process of nuclides in the superheavy region is critical in
investigating new elements beyond oganesson and the island of stability. This paper …

We have obtained the constraints on the density dependence of the symmetry energy from
neutron-skin thickness data by parity-violating electron scatterings and neutron-star …

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 …

The extended kernel ridge regression (EKRR) method with odd–even effects was adopted to
improve the description of the nuclear charge radius using five commonly used nuclear …

The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions helps us
understand the CP symmetry breaking in strong interactions and the topological nature of …