Related papers: Systematics on ground-state energies of nuclei wit…
The artificial neural networks (ANNs) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, by using (ANNs), we have constructed a formula for the nuclear charge…
The binding energy (BE) or mass is one of the most fundamental properties of an atomic nucleus. Precise binding energies are vital inputs for many nuclear physics and nuclear astrophysics studies. However, due to the complexity of atomic…
In recent years, artificial neural networks and their applications for large data sets have became a crucial part of scientific research. In this work, we implement the Multilayer Perceptron (MLP), which is a class of feedforward artificial…
In pursuing the essential elements of nuclear binding, we compute ground-state properties of atomic nuclei with up to $A=20$ nucleons, using as input a leading order pionless effective field theory Hamiltonian. A variational Monte Carlo…
In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of…
In this work, we have studied ground-state properties of both even-even and odd Nd isotopes within Hartree-Fock-Bogoliubov method with SLy5 Skyrme force in which the pairing strength has been generalized with a new proposed formula. We…
Selfconsistent Hartree-Fock-Bogoliubov (HFB) calculations have been performed with the Gogny force for nuclei along several constant Z and constant N chains, with the purpose of extracting the macroscopic part of the binding energy using…
A relativistic Hartree-Bogoliubov (RHB) model based on quark-meson coupling is developed, with a new parametrization derived from experimental observables. Using this model, we systematically investigate the ground-state properties of…
The ground-state properties of 64Ge, 68Se, 72Kr and 76Sr (N=Z) nuclei have been investigated by using Hartree-Fock-Bogolibov (HFB)method with Sly4 Skyrme forces and Relativistic Mean Field (RMF) model with NL3 and recently proposed DEFNE…
Nuclear binding energies and two-neutron separation energies are analyzed starting from the liquid-drop model and the nuclear shell model in order to describe the global trends of the above observables. We subsequently concentrate on the…
A systematic study of the ground state properties of transitional nuclei W, Os and Pt is conducted with the help of Skyrme Hartree-Fock-Bogoliubov theory. Different Skyrme interactions are employed in the study. Two different bases,…
Background: Heavy atomic nuclei are often described using the Hartree-Fock-Bogoliubov (HFB) method. In principle, this approach takes into account Pauli effects and pairing correlations while other correlation effects are mimicked through…
We have developed a new Hartree-Fock-Bogoliubov (HFB) code which has been specifically designed to study ground state properties of nuclei near the neutron and proton drip lines. The unique feature of our code is that it takes into account…
Statistical modeling of data sets by neural-network techniques is offered as an alternative to traditional semiempirical approaches to global modeling of nuclear properties. New results are presented to support the position that such novel…
We compare the systematics of binding energies computed within the standard and extended versions of the relativistic mean-field (RMF) model and the Skyrme Hartree-Fock (SHF) model. The general trends for the binding energies for…
Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy close to the ab-initio methods used to build them. Besides modeling potential energy surfaces, neural-nets can…
In this paper, we demonstrate the expressibility of artificial neural networks (ANNs) in quantum many-body physics by showing that a feed-forward neural network with a small number of hidden layers can be trained to approximate with high…
We build and train the artificial neural network model (ANN) based on the experimental $\alpha$-decay energy ($Q_{\alpha}$) data. Besides decays between the ground states of parent and daughter nuclei, decays from the ground state of parent…
Artificial Neural Networks (ANNs) are currently being used as function approximators in many state-of-the-art Reinforcement Learning (RL) algorithms. Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy…
We deal with the systematics of one and two proton separation energies as predicted by our latest global model for the masses of nuclides developed with the use of neural networks. Among others, such systematics is useful as input to the…