Learning pairing symmetries in disordered superconductors using spin-polarized local density of states
We construct an artificial neural network to study the pairing symmetries in disordered superconductors. For Hamiltonians on square lattice with s-wave, d-wave, and nematic pairing potentials, we use the spin-polarized local density of states near a magnetic impurity in the clean system to train the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2020-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ab8261 |
| _version_ | 1797750259791691776 |
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| author | Liang Chen Chen-Xi Wang Rong-Sheng Han Ye-Qi Zhang |
| author_facet | Liang Chen Chen-Xi Wang Rong-Sheng Han Ye-Qi Zhang |
| author_sort | Liang Chen |
| collection | DOAJ |
| description | We construct an artificial neural network to study the pairing symmetries in disordered superconductors. For Hamiltonians on square lattice with s-wave, d-wave, and nematic pairing potentials, we use the spin-polarized local density of states near a magnetic impurity in the clean system to train the neural network. We find that, when the depth of the artificial neural network is sufficient large, it will have the power to predict the pairing symmetries in disordered superconductors. In a large parameter regime of the potential disorder, the artificial neural network predicts the correct pairing symmetries with relatively high confidences. |
| first_indexed | 2024-03-12T16:30:06Z |
| format | Article |
| id | doaj.art-09d0bd9803b24920ad9d2c3d493c5865 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:30:06Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-09d0bd9803b24920ad9d2c3d493c58652023-08-08T15:31:12ZengIOP PublishingNew Journal of Physics1367-26302020-01-0122505301510.1088/1367-2630/ab8261Learning pairing symmetries in disordered superconductors using spin-polarized local density of statesLiang Chen0Chen-Xi Wang1Rong-Sheng Han2Ye-Qi Zhang3Mathematics and Physics Department, North China Electric Power University , Beijing, 102206, People’s Republic of ChinaMathematics and Physics Department, North China Electric Power University , Beijing, 102206, People’s Republic of ChinaMathematics and Physics Department, North China Electric Power University , Beijing, 102206, People’s Republic of ChinaMathematics and Physics Department, North China Electric Power University , Beijing, 102206, People’s Republic of ChinaWe construct an artificial neural network to study the pairing symmetries in disordered superconductors. For Hamiltonians on square lattice with s-wave, d-wave, and nematic pairing potentials, we use the spin-polarized local density of states near a magnetic impurity in the clean system to train the neural network. We find that, when the depth of the artificial neural network is sufficient large, it will have the power to predict the pairing symmetries in disordered superconductors. In a large parameter regime of the potential disorder, the artificial neural network predicts the correct pairing symmetries with relatively high confidences.https://doi.org/10.1088/1367-2630/ab8261pairing symmetryspin-polarized spectroscopymachine learning |
| spellingShingle | Liang Chen Chen-Xi Wang Rong-Sheng Han Ye-Qi Zhang Learning pairing symmetries in disordered superconductors using spin-polarized local density of states New Journal of Physics pairing symmetry spin-polarized spectroscopy machine learning |
| title | Learning pairing symmetries in disordered superconductors using spin-polarized local density of states |
| title_full | Learning pairing symmetries in disordered superconductors using spin-polarized local density of states |
| title_fullStr | Learning pairing symmetries in disordered superconductors using spin-polarized local density of states |
| title_full_unstemmed | Learning pairing symmetries in disordered superconductors using spin-polarized local density of states |
| title_short | Learning pairing symmetries in disordered superconductors using spin-polarized local density of states |
| title_sort | learning pairing symmetries in disordered superconductors using spin polarized local density of states |
| topic | pairing symmetry spin-polarized spectroscopy machine learning |
| url | https://doi.org/10.1088/1367-2630/ab8261 |
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