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...

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Bibliographic Details
Published in:New journal of physics 2020-05, Vol.22 (5), p.53015
Main Authors: Chen, Liang, Wang, Chen-Xi, Han, Rong-Sheng, Zhang, Ye-Qi
Format: Article
Language:English
Subjects:
Artificial neural networks
Density of states
machine learning
Neural networks
pairing symmetry
Physics
spin-polarized spectroscopy
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Summary: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.
ISSN:1367-2630
1367-2630
DOI:10.1088/1367-2630/ab8261