Neural network emulation of spontaneous fission

Background: Large-scale computations of fission properties are an important ingredient for nuclear reaction network calculations simulating rapid neutron-capture process (the r process) nucleosynthesis. Due to the large number of fissioning nuclei potentially contributing to the r process, a microsc...

ver descrição completa

Detalhes bibliográficos
Autores: Lay, Daniel, Flynn, Eric, Nazarewicz, Witold, Neufcourt, Léo, Giuliani, Samuel Andrea
Tipo de documento: artigo
Data de publicação:2024
País:España
Recursos:Universidad Autónoma de Madrid
Repositório:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglês
OAI Identifier:oai:repositorio.uam.es:10486/715200
Acesso em linha:http://hdl.handle.net/10486/715200
https://dx.doi.org/10.1103/PhysRevC.109.044305
Access Level:Acceso aberto
Palavra-chave:neural network emulation
spontaneous fission
r process
fission
Física
id ES_7171855ad985d217a1113ee8ef2c5bcd
oai_identifier_str oai:repositorio.uam.es:10486/715200
network_acronym_str ES
network_name_str España
repository_id_str
spelling Neural network emulation of spontaneous fissionLay, DanielFlynn, EricNazarewicz, WitoldNeufcourt, LéoGiuliani, Samuel Andreaneural network emulationspontaneous fissionr processfissionFísicaBackground: Large-scale computations of fission properties are an important ingredient for nuclear reaction network calculations simulating rapid neutron-capture process (the r process) nucleosynthesis. Due to the large number of fissioning nuclei potentially contributing to the r process, a microscopic description of fission based on nuclear density functional theory (DFT) is computationally challenging. Purpose: We explore the use of neural networks (NNs) to construct DFT emulators capable of predicting potential energy surfaces and collective inertia tensors across the whole nuclear chart, starting from a minimal set of DFT calculations. Methods: We use constrained Hartree-Fock-Bogoliubov (HFB) calculations to predict the potential energy and collective inertia tensor in the axial quadrupole and octupole collective coordinates, for a set of nuclei in the r-process region. We then employ NNs to emulate the HFB energy and collective inertia tensor across the considered region of the nuclear chart. Least-action pathways characterizing spontaneous fission half-lives and fragment yields are then obtained by means of the nudged elastic band method. Results: The potential energy predicted by NNs agrees with the DFT value to within a root-mean-square error of 500 keV, and the collective inertia components agree to within an order of magnitude. These results are largely independent of the NN architecture. The exit points on the outer turning line are found to be well emulated. For the spontaneous fission half-lives the NN emulation provides values that are found to agree with the DFT predictions within a factor of 103 across more than 70 orders of magnitude. Conclusions: Neural networks are able to emulate the potential energy and collective inertia well enough to reasonably predict physical observables. Future directions of study, such as the inclusion of additional collective degrees of freedom and active learning, will improve the predictive power of microscopic theory and further enable large-scale fission studiesThis work was supported by the U.S. Department of Energy under Award Nos. DOE-DE-NA0004074 (NNSA, the Stewardship Science Academic Alliances program), DE-SC0013365 (Office of Science), DE-SC0023175 (Office of Advanced Scientific Computing Research and Office of Nuclear Physics, Scientific Discovery through Advanced Computing), and DE-SC0024586 (STREAMLINE collaboration); and by the Spanish Agencia Estatal de Investigación (AEI) of the Ministry of Science and Innovation (MCIN) under Grant Agreements No. PID2021-127890NB-I00 and RYC2021-031880-I funded by MCIN/AEI/10.13039/501100011033 and the European Union-“NextGenerationEU/PRTR”American Physical SocietyDepartamento de Física TeóricaFacultad de Ciencias20242024-04-02research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/715200https://dx.doi.org/10.1103/PhysRevC.109.044305reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7152002026-06-23T12:46:27Z
dc.title.none.fl_str_mv Neural network emulation of spontaneous fission
title Neural network emulation of spontaneous fission
spellingShingle Neural network emulation of spontaneous fission
Lay, Daniel
neural network emulation
spontaneous fission
r process
fission
Física
title_short Neural network emulation of spontaneous fission
title_full Neural network emulation of spontaneous fission
title_fullStr Neural network emulation of spontaneous fission
title_full_unstemmed Neural network emulation of spontaneous fission
title_sort Neural network emulation of spontaneous fission
dc.creator.none.fl_str_mv Lay, Daniel
Flynn, Eric
Nazarewicz, Witold
Neufcourt, Léo
Giuliani, Samuel Andrea
author Lay, Daniel
author_facet Lay, Daniel
Flynn, Eric
Nazarewicz, Witold
Neufcourt, Léo
Giuliani, Samuel Andrea
author_role author
author2 Flynn, Eric
Nazarewicz, Witold
Neufcourt, Léo
Giuliani, Samuel Andrea
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Física Teórica
Facultad de Ciencias
dc.subject.none.fl_str_mv neural network emulation
spontaneous fission
r process
fission
Física
topic neural network emulation
spontaneous fission
r process
fission
Física
description Background: Large-scale computations of fission properties are an important ingredient for nuclear reaction network calculations simulating rapid neutron-capture process (the r process) nucleosynthesis. Due to the large number of fissioning nuclei potentially contributing to the r process, a microscopic description of fission based on nuclear density functional theory (DFT) is computationally challenging. Purpose: We explore the use of neural networks (NNs) to construct DFT emulators capable of predicting potential energy surfaces and collective inertia tensors across the whole nuclear chart, starting from a minimal set of DFT calculations. Methods: We use constrained Hartree-Fock-Bogoliubov (HFB) calculations to predict the potential energy and collective inertia tensor in the axial quadrupole and octupole collective coordinates, for a set of nuclei in the r-process region. We then employ NNs to emulate the HFB energy and collective inertia tensor across the considered region of the nuclear chart. Least-action pathways characterizing spontaneous fission half-lives and fragment yields are then obtained by means of the nudged elastic band method. Results: The potential energy predicted by NNs agrees with the DFT value to within a root-mean-square error of 500 keV, and the collective inertia components agree to within an order of magnitude. These results are largely independent of the NN architecture. The exit points on the outer turning line are found to be well emulated. For the spontaneous fission half-lives the NN emulation provides values that are found to agree with the DFT predictions within a factor of 103 across more than 70 orders of magnitude. Conclusions: Neural networks are able to emulate the potential energy and collective inertia well enough to reasonably predict physical observables. Future directions of study, such as the inclusion of additional collective degrees of freedom and active learning, will improve the predictive power of microscopic theory and further enable large-scale fission studies
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-04-02
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/715200
https://dx.doi.org/10.1103/PhysRevC.109.044305
url http://hdl.handle.net/10486/715200
https://dx.doi.org/10.1103/PhysRevC.109.044305
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410652861235200
score 15.198674

Registros relacionados