Extrapolation to infinite model space of no-core shell model calculations using machine learning.
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| Title: | Extrapolation to infinite model space of no-core shell model calculations using machine learning. |
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| Authors: | Mazur, Aleksandr1 (AUTHOR) 000287@togudv.ru, Sharypov, Roman1 (AUTHOR) 2017104939@togudv.ru, Shirokov, Andrey2 (AUTHOR) shirokov@nucl-th.sinp.msu.ru |
| Source: | International Journal of Modern Physics E: Nuclear Physics. May2026, Vol. 35 Issue 5, p1-11. 11p. |
| Subject Terms: | *Artificial neural networks, *Extrapolation, *Nuclear physics, *Nucleon-nucleon interactions, *Nuclear shell theory, *Atomic nucleus, *Nuclear structure, *Machine learning |
| Abstract: | An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 N N interaction in different model spaces and with different values of the NCSM basis parameter ℏ Ω for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for 6Li, 6He and the unbound 6Be, as well as the excited (3 + , 0) and (0 + , 1) states of 6Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well. In contrast, radii of unbound states in 6Be and 6Li do not stabilize. These results demonstrate that machine-learning extrapolations can extend the reach of ab initio nuclear structure calculations with reliable accuracy. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194092339 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Extrapolation to infinite model space of no-core shell model calculations using machine learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mazur%2C+Aleksandr%22">Mazur, Aleksandr</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 000287@togudv.ru</i><br /><searchLink fieldCode="AR" term="%22Sharypov%2C+Roman%22">Sharypov, Roman</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2017104939@togudv.ru</i><br /><searchLink fieldCode="AR" term="%22Shirokov%2C+Andrey%22">Shirokov, Andrey</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> shirokov@nucl-th.sinp.msu.ru</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Modern+Physics+E%3A+Nuclear+Physics%22">International Journal of Modern Physics E: Nuclear Physics</searchLink>. May2026, Vol. 35 Issue 5, p1-11. 11p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Extrapolation%22">Extrapolation</searchLink><br />*<searchLink fieldCode="DE" term="%22Nuclear+physics%22">Nuclear physics</searchLink><br />*<searchLink fieldCode="DE" term="%22Nucleon-nucleon+interactions%22">Nucleon-nucleon interactions</searchLink><br />*<searchLink fieldCode="DE" term="%22Nuclear+shell+theory%22">Nuclear shell theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Atomic+nucleus%22">Atomic nucleus</searchLink><br />*<searchLink fieldCode="DE" term="%22Nuclear+structure%22">Nuclear structure</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 N N interaction in different model spaces and with different values of the NCSM basis parameter ℏ Ω for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for 6Li, 6He and the unbound 6Be, as well as the excited (3 + , 0) and (0 + , 1) states of 6Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well. In contrast, radii of unbound states in 6Be and 6Li do not stabilize. These results demonstrate that machine-learning extrapolations can extend the reach of ab initio nuclear structure calculations with reliable accuracy. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218301326410144 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Extrapolation Type: general – SubjectFull: Nuclear physics Type: general – SubjectFull: Nucleon-nucleon interactions Type: general – SubjectFull: Nuclear shell theory Type: general – SubjectFull: Atomic nucleus Type: general – SubjectFull: Nuclear structure Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Extrapolation to infinite model space of no-core shell model calculations using machine learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mazur, Aleksandr – PersonEntity: Name: NameFull: Sharypov, Roman – PersonEntity: Name: NameFull: Shirokov, Andrey IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02183013 Numbering: – Type: volume Value: 35 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Modern Physics E: Nuclear Physics Type: main |
| ResultId | 1 |
