Related papers: Towards next-generation optical potentials for nuc…
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human…
Recent progress in the numerical solution of the nuclear many-body problem and in the development of nuclear Hamiltonians rooted in Quantum Chromodynamics, has opened the door to first-principle computations of nuclear reactions. In this…
Several nucleon-nucleon potentials, Paris, Nijmegen, Argonne, and those derived by quantum inversion, which describe the NN interaction for T-lab below 300$ MeV are extended in their range of application as NN optical models. Extensions are…
A global nucleon-nucleus optical potential for elastic scattering has been produced which replicates experimental data to high accuracy and compares well with other recently formulated potentials. The calculation that has been developed…
In this paper, we construct a new phenomenological isospin dependent global neutron-nucleus optical model potential. Based on the existing experimental data of elastic scattering angular distributions for neutron as projectile, we obtain a…
Large-scale atomistic computer simulations of materials rely on interatomic potentials providing computationally efficient predictions of energy and Newtonian forces. Traditional potentials have served in this capacity for over three…
Physically motivated quantum algorithms for specific near-term quantum hardware will likely be the next frontier in quantum information science. Here, we show how many of the features of neural networks for machine learning can naturally be…
A spherical optical model potential (OMP) containing a dispersive term is used to fit the available experimental database of angular distribution and total cross section data for n + Al27 covering the energy range 0.1- 250 MeV using…
One important ingredient for many applications of nuclear physics to astrophysics, nuclear energy, and stockpile stewardship are cross sections for reactions of neutrons with rare isotopes. Since direct measurements are often not feasible,…
Emerging artificial intelligence applications across the domains of computer vision, natural language processing, graph processing, and sequence prediction increasingly rely on deep neural networks (DNNs). These DNNs require significant…
The excited state dynamics of chromophores in complex environments determine a range of vital biological and energy capture processes. Time-resolved, multidimensional optical spectroscopies provide a key tool to investigate these processes.…
Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase…
We derived microscopic optical potentials (OPs) for elastic nucleon-nucleus scattering within the framework of chiral effective field theories at the first-order term of the spectator expansion of the Watson multiple-scattering theory and…
Developing high-precision models of the nuclear force and propagating the associated uncertainties in quantum many-body calculations of nuclei and nuclear matter remain key challenges for ab initio nuclear theory. In the present work we…
Microscopic descriptions of exotic nuclei are the subject of much experimental and theoretical effort. Not only are such important in their own right but are also necessary for applications in nuclear astrophysics. Evaluations of model wave…
A recently derived dispersive optical model potential (DOMP) for $^{208}$Pb is extended to consider the non-locality in the real potential and the shell-gap in the definition of the nuclear imaginary potentials near the Fermi energy. The…
Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of…
Machine learning interatomic potentials (MLIPs) based on a large dataset obtained by density functional theory (DFT) calculation have been developed recently. This study gives both conceptual and practical bases for the high accuracy of…
$(\alpha,\gamma)$ and $(\alpha$,n) reaction cross sections recently measured close to the reaction thresholds are rather well described by a previously developed regional optical potential. Thus, particular features of the $\alpha$-particle…
An optical quantum memory can be broadly defined as a system capable of storing a useful quantum state through interaction with light at optical frequencies. During the last decade, intense research was devoted to their development, mostly…