Alex Gnech, Bryce Fore, Anthony J Tropiano, Alessandro Lovato
{"title":"Distilling the Essential Elements of Nuclear Binding via Neural-Network Quantum States.","authors":"Alex Gnech, Bryce Fore, Anthony J Tropiano, Alessandro Lovato","doi":"10.1103/PhysRevLett.133.142501","DOIUrl":null,"url":null,"abstract":"<p><p>To distill the essential elements of nuclear binding, we seek the simplest Hamiltonian capable of modeling atomic nuclei with percent-level accuracy. A critical aspect of this endeavor consists of accurately solving the quantum many-body problem without incurring an exponential computing cost with the number of nucleons. We address this challenge by leveraging a variational Monte Carlo method based on a highly expressive neural-network quantum state ansatz. In addition to computing binding energies and charge radii of nuclei with up to A=20 nucleons, by evaluating their magnetic moments, we demonstrate that neural-network quantum states are able to correctly capture the self-emerging nuclear shell structure. To this end, we introduce a novel computational protocol based on adding an external magnetic field to the nuclear Hamiltonian, which allows the neural network to learn the preferred polarization of the nucleus within the given magnetic field.</p>","PeriodicalId":20069,"journal":{"name":"Physical review letters","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical review letters","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1103/PhysRevLett.133.142501","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
To distill the essential elements of nuclear binding, we seek the simplest Hamiltonian capable of modeling atomic nuclei with percent-level accuracy. A critical aspect of this endeavor consists of accurately solving the quantum many-body problem without incurring an exponential computing cost with the number of nucleons. We address this challenge by leveraging a variational Monte Carlo method based on a highly expressive neural-network quantum state ansatz. In addition to computing binding energies and charge radii of nuclei with up to A=20 nucleons, by evaluating their magnetic moments, we demonstrate that neural-network quantum states are able to correctly capture the self-emerging nuclear shell structure. To this end, we introduce a novel computational protocol based on adding an external magnetic field to the nuclear Hamiltonian, which allows the neural network to learn the preferred polarization of the nucleus within the given magnetic field.
期刊介绍:
Physical review letters(PRL)covers the full range of applied, fundamental, and interdisciplinary physics research topics:
General physics, including statistical and quantum mechanics and quantum information
Gravitation, astrophysics, and cosmology
Elementary particles and fields
Nuclear physics
Atomic, molecular, and optical physics
Nonlinear dynamics, fluid dynamics, and classical optics
Plasma and beam physics
Condensed matter and materials physics
Polymers, soft matter, biological, climate and interdisciplinary physics, including networks