Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor
Abstract We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function M (F 2), which is necessary to understand phase transition behav...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of High Energy Physics |
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| Online Access: | https://doi.org/10.1007/JHEP02(2025)077 |
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| _version_ | 1849768137622290432 |
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| author | Sejin Kim Kyung Kiu Kim Yunseok Seo |
| author_facet | Sejin Kim Kyung Kiu Kim Yunseok Seo |
| author_sort | Sejin Kim |
| collection | DOAJ |
| description | Abstract We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function M (F 2), which is necessary to understand phase transition behavior. This mass function describes a nonlinear interaction between superconducting order and charge carrier density. We introduce positional embedding layers to improve the learning process in our algorithm, and the Adam optimization is used to predict the critical temperature data via holographic calculation with appropriate accuracy. Consideration of the positional embedding layers is motivated by the transformer model of natural-language processing in the artificial intelligence (AI) field. We obtain holographic models that reproduce borderlines of the normal and superconducting phases provided by actual data. Our work is the first holographic attempt to match phase transition data quantitatively obtained from experiments. Also, the present work offers a new methodology for data-based holographic models. |
| format | Article |
| id | doaj-art-9d60c70448f348ceb4f5369a93e2ef9e |
| institution | DOAJ |
| issn | 1029-8479 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj-art-9d60c70448f348ceb4f5369a93e2ef9e2025-08-20T03:03:55ZengSpringerOpenJournal of High Energy Physics1029-84792025-02-012025212310.1007/JHEP02(2025)077Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductorSejin Kim0Kyung Kiu Kim1Yunseok Seo2College of General Education, Kookmin UniversityCollege of General Education, Kookmin UniversityCollege of General Education, Kookmin UniversityAbstract We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function M (F 2), which is necessary to understand phase transition behavior. This mass function describes a nonlinear interaction between superconducting order and charge carrier density. We introduce positional embedding layers to improve the learning process in our algorithm, and the Adam optimization is used to predict the critical temperature data via holographic calculation with appropriate accuracy. Consideration of the positional embedding layers is motivated by the transformer model of natural-language processing in the artificial intelligence (AI) field. We obtain holographic models that reproduce borderlines of the normal and superconducting phases provided by actual data. Our work is the first holographic attempt to match phase transition data quantitatively obtained from experiments. Also, the present work offers a new methodology for data-based holographic models.https://doi.org/10.1007/JHEP02(2025)077Gauge-Gravity CorrespondenceHolography and Condensed Matter Physics (AdS/CMT) |
| spellingShingle | Sejin Kim Kyung Kiu Kim Yunseok Seo Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor Journal of High Energy Physics Gauge-Gravity Correspondence Holography and Condensed Matter Physics (AdS/CMT) |
| title | Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor |
| title_full | Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor |
| title_fullStr | Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor |
| title_full_unstemmed | Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor |
| title_short | Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor |
| title_sort | phase diagram from nonlinear interaction between superconducting order and density toward data based holographic superconductor |
| topic | Gauge-Gravity Correspondence Holography and Condensed Matter Physics (AdS/CMT) |
| url | https://doi.org/10.1007/JHEP02(2025)077 |
| work_keys_str_mv | AT sejinkim phasediagramfromnonlinearinteractionbetweensuperconductingorderanddensitytowarddatabasedholographicsuperconductor AT kyungkiukim phasediagramfromnonlinearinteractionbetweensuperconductingorderanddensitytowarddatabasedholographicsuperconductor AT yunseokseo phasediagramfromnonlinearinteractionbetweensuperconductingorderanddensitytowarddatabasedholographicsuperconductor |