Discovering nuclear models from symbolic machine learning
Abstract Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether symbolic Mach...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-02023-2 |
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| _version_ | 1849774474060103680 |
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| author | Jose M. Munoz Silviu M. Udrescu Ronald F. Garcia Ruiz |
| author_facet | Jose M. Munoz Silviu M. Udrescu Ronald F. Garcia Ruiz |
| author_sort | Jose M. Munoz |
| collection | DOAJ |
| description | Abstract Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems. |
| format | Article |
| id | doaj-art-b3e518b8bc5b4a6eb6bd630d29a03609 |
| institution | DOAJ |
| issn | 2399-3650 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj-art-b3e518b8bc5b4a6eb6bd630d29a036092025-08-20T03:01:41ZengNature PortfolioCommunications Physics2399-36502025-03-01811810.1038/s42005-025-02023-2Discovering nuclear models from symbolic machine learningJose M. Munoz0Silviu M. Udrescu1Ronald F. Garcia Ruiz2Massachusetts Institute of TechnologyMassachusetts Institute of TechnologyMassachusetts Institute of TechnologyAbstract Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.https://doi.org/10.1038/s42005-025-02023-2 |
| spellingShingle | Jose M. Munoz Silviu M. Udrescu Ronald F. Garcia Ruiz Discovering nuclear models from symbolic machine learning Communications Physics |
| title | Discovering nuclear models from symbolic machine learning |
| title_full | Discovering nuclear models from symbolic machine learning |
| title_fullStr | Discovering nuclear models from symbolic machine learning |
| title_full_unstemmed | Discovering nuclear models from symbolic machine learning |
| title_short | Discovering nuclear models from symbolic machine learning |
| title_sort | discovering nuclear models from symbolic machine learning |
| url | https://doi.org/10.1038/s42005-025-02023-2 |
| work_keys_str_mv | AT josemmunoz discoveringnuclearmodelsfromsymbolicmachinelearning AT silviumudrescu discoveringnuclearmodelsfromsymbolicmachinelearning AT ronaldfgarciaruiz discoveringnuclearmodelsfromsymbolicmachinelearning |