Related papers: Nuclear binding energy predictions based on BP neu…
We have performed r-process calculations for matter ejected dynamically in neutron star mergers based on a complete set of trajectories from a three-dimensional relativistic smoothed particle hydrodynamic simulation. Our calculations…
Atomic experiments bring meaningful and valuable information on fundamental symmetries. The hypothesis of a large ($\sim 100$ eV) P-odd weak matrix element between single-particle states in heavy nuclei is inconsistent with the results of…
Using the new results on coherent elastic neutrino-nucleus scattering data in cesium-iodide provided by the COHERENT experiment, we determine a new measurement of the average neutron rms radius of $^{133}\text{Cs}$ and $^{127}\text{I}$. In…
We present here the mass excesses, binding energies, one- and two- neutron, one and two- proton and \alpha-particle separation energies of 6727 nuclei in the ranges 4 \leq Z \leq 120 and 8 \leq A \leq 303 calculated in the infinite nuclear…
Among all nuclear ground-state properties, atomic masses are highly specific for each particular combination of N and Z and the data obtained apply to a variety of physics topics. One of the most crucial questions to be addressed in mass…
The Statistical Model of the Early Stage, SMES, describes a transition between confined and deconfined phases of strongly interacting matter created in nucleus-nucleus collisions. The model was formulated in the late 1990s for central Pb+Pb…
Contemporary neuroscience has embraced network science to study the complex and self-organized structure of the human brain; one of the main outstanding issues is that of inferring from measure data, chiefly functional Magnetic Resonance…
A formation zone intranuclear cascade model is applied to peripheral nucleus-nucleus collisions. We calculate the excitation energies of prefragments, treat their further nuclear disintegration and introduce a model for nuclear deexcitation…
Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness, or a quantitative result. In many such tasks, the point…
We present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model and predict nuclear dipole responses. We train the network on a dataset of experimentally measured…
New parameter-sets of the semi-realistic nucleon-nucleon interaction are developed, by modifying the M3Y interaction but maintaining the tensor channels and the longest-range central channels. The modification is made so as to reproduce…
In this work, a refined Bayesian neural network (BNN) based approach with six inputs including the proton number, mass number, and engineered features associated with the pairing effect, shell effect, isospin effect, and ``abnormal" shape…
Recent work on using density dependent $\Lambda$-nuclear optical potentials in calculations of $\Lambda$-hypernuclear binding energies is reviewed. It is found that all known $\Lambda$ binding energies in the mass range $16 \leq A \leq 208$…
The fusion mechanism of reactions involving even-even $^{112-124}$Sn, doubly magic $^{132}$Sn, $^{208}$Pb as targets, and $^{64}$Ni as the projectile is explored within the relativistic mean field (RMF) formalism. The main aim of choosing…
The algebraic reformulation of molecular Quantum Electrodynamics (mQED) at finite temperatures is applied to Nuclear Magnetic Resonance (NMR) in order to provide a foundation for the reconstruction of much more detailed molecular…
The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. We explore a novel approach that combines…
We demonstrate a machine learning based approach which can learn the time-dependent electronic excitation dynamics of small molecules subjected to ion irradiation. Ensembles of recurrent neural networks are trained on data generated by…
Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions.…
The article proposes a conceptual approach for evaluating the impact of engineered nanoparticles (NPs) on the functionality of small biomolecules. The developed machine learning (ML) model is based on in-silico 13C NMR spectroscopy chemical…
We discuss the least-square and linear-regression methods, which are relevant for a reliable determination of good nuclear-mass-model parameter sets and their errors. In this perspective, we define exact and inaccurate models and point out…