Related papers: A Bayesian mixture model approach to quantifying t…
We investigate possible correlations between the stiffness of the symmetry energy at saturation density, the so-called $L$ parameter, and the neutron skin thickness of ${^{48}}$Ca and ${^{208}}$Pb, for which the recent measurements from the…
The equation of state (EOS) of dense matter is an essential ingredient for numerical simulations of core-collapse supernovae and neutron star mergers. The properties of matter near and above nuclear saturation density are uncertain, which…
An equation of state(EOS) of nuclear matter with explicit inclusion of a spin-isospin dependent force is constructed from a finite range, momentum and density dependent effective interaction. This EOS is found to be in good agreement with…
Determining the Equation of State (EOS) of dense neutron-rich nuclear matter is a shared goal of both nuclear physics and astrophysics. Except possible phase transitions, the density dependence of nuclear symmetry \esym is the most…
The low density nuclear matter equation of state is strongly constrained by nuclear properties, however, for constraining the high density equation of state it is necessary to resort to indirect information obtained from the observation of…
We develop a new nonparametric method to reconstruct the Equation of State (EoS) of Neutron Star with multimessenger data. As an universal function approximator, the Feed-Forward Neural Network (FFNN) with one hidden layer and a sigmoidal…
Density Functional Theory (DFT) is a powerful and accurate tool exploited in Nuclear Physics to investigate the ground-state and some collective properties of nuclei along the whole nuclear chart. Models based on DFT are, however, not…
Chiral effective field theory ($\chi$EFT) has proved to be a powerful microscopic framework for predicting the properties of neutron-rich nuclear matter with quantified theoretical uncertainties up to about twice the nuclear saturation…
The present contribution does not aim at replacing the huge and often excellent literature on DFT for atomic nuclei, but tries to provide an updated introduction to this topic. The goal would be, ideally, to help a fresh M.Sc. or Ph.D.…
Improving the predictive capability of molecular properties in ab initio simulations is essential for advanced material discovery. Despite recent progress making use of machine learning, utilizing deep neural networks to improve quantum…
The density functional theory (DFT) is based on the existence and uniqueness of a universal functional $E[\rho]$, which determines the dependence of the total energy on single-particle density distributions. However, DFT says nothing about…
In this proceeding, we have presented some highlight results on the constraints of the nuclear matter equation of state (EOS) from the data of nucleus resonance and neutron-skin thickness using the Bayesian approach based on the…
The increasing richness of data related to cold dense matter, from laboratory experiments to neutron-star observations, requires a framework for constraining the properties of such matter that makes use of all relevant information. Here, we…
Covariant density functional theory (CDFT) is a modern theoretical tool for the description of nuclear structure phenomena. The current investigation aims at the global assessment of the accuracy of the description of the ground state…
In this review, we will discuss the results of our recent work to study the general optimization of the pure isovector parameters of the popular relativistic mean-field (RMF) and Skyrme-Hartree-Fock (SHF) nuclear energy-density functionals…
Ground-state electronic structure calculations using Kohn-Sham density functional theory (KS-DFT) offer an unprecedented balance between efficiency and accuracy, now paradigmatic to the fields of quantum chemistry and condensed matter…
Nuclear energy density functional theory (DFT) provides a microscopic approach to describing nuclear masses. By incorporating pairing correlations and deformation within DFT, nuclear masses can be predicted with sub-MeV accuracy. A crucial…
We present a probabilistic model for the QCD critical endpoint (CEP) and the equation of state (EOS) at $\beta$-equilibrium, constrained by neutron-star observations. Using a hybrid framework that combines the holographic V-QCD model with…
The swift progression of machine learning (ML) has not gone unnoticed in the realm of statistical mechanics. ML techniques have attracted attention by the classical density-functional theory (DFT) community, as they enable discovery of…
The description of fluid mixtures molecular behavior is significant for various industry fields due to the complex composition of fluid found in nature. Statistical mechanics approaches use intermolecular interaction potential to predict…