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Machine learning prediction of superconducting pairing potential for quasi-one-dimensional disordered s-wave superconductors

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10.64214/jmsn.01.02005
Published 2025-12-30 , received 2025-06-18
Letter , 01-02005, Volume 1, Issue 2
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Neverov V. et al. Machine learning prediction of superconducting pairing potential for quasi-one-dimensional disordered s-wave superconductors // Mesoscience & Nanotechnology. 2025. Vol. 1. No. 2. 01-02005
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Neverov V., Krasavin A., Tomayeva M., Vagov A. Machine learning prediction of superconducting pairing potential for quasi-one-dimensional disordered s-wave superconductors // Mesoscience & Nanotechnology. 2025. Vol. 1. No. 2. 01-02005
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TY - JOUR
DO - 10.64214/jmsn.01.02005
UR - https://jmsn.press/publications/10.64214/jmsn.01.02005
TI - Machine learning prediction of superconducting pairing potential for quasi-one-dimensional disordered s-wave superconductors
T2 - Mesoscience & Nanotechnology
AU - Neverov, Vyacheslav
AU - Krasavin, Andrey
AU - Tomayeva, Munisa
AU - Vagov, Alexey
PY - 2025
DA - 2025/12/30
PB - Treatise LLC
SP - 01-02005
IS - 2
VL - 1
ER -
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@article{2025_Neverov,
author = {Vyacheslav Neverov and Andrey Krasavin and Munisa Tomayeva and Alexey Vagov},
title = {Machine learning prediction of superconducting pairing potential for quasi-one-dimensional disordered s-wave superconductors},
journal = {Mesoscience & Nanotechnology},
year = {2025},
volume = {1},
publisher = {Treatise LLC},
month = {Dec},
url = {https://jmsn.press/publications/10.64214/jmsn.01.02005},
number = {2},
doi = {10.64214/jmsn.01.02005}
}
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Neverov, Vyacheslav, et al. “Machine learning prediction of superconducting pairing potential for quasi-one-dimensional disordered s-wave superconductors.” Mesoscience & Nanotechnology, vol. 1, no. 2, Dec. 2025, pp. 01-02005. https://jmsn.press/publications/10.64214/jmsn.01.02005.
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Keywords

Bogoliubov-de Gennes equations
Disorder
Low-dimensional systems
Machine learning
Neural networks
Superconductivity

Abstract

We developed a machine learning algorithm designed to predict the spatial distribution of the pairing potential in a quasi-one-dimensional superconductor based on a given disorder profile. This approach uses the ability of neural networks to learn mappings between disorder configurations and the corresponding superconducting pairing potential, bypassing the need for iterative numerical solutions of the Bogoliubov-de Gennes equations.

The final text of the article will be published soon.

References