Scalable machine learning approach to disordered s-wave superconductors
Vyacheslav D. Neverov, Alexander E. Lukyanov, Andrey V. Krasavin, Alexei Vagov
DOI 10.1103/xnzb-txqy · Physical Review B
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Abstract
We develop a neural network approach to solve the self-consistent Bogoliubov-de Gennes equations in strongly disordered s-wave superconductors. The method accurately reproduces inhomogeneous gap distributions and generalizes to system sizes far larger than those used in training. It reduces computational scaling from O(N6) to O(N2), enabling quantitative analysis of percolation phenomena and the superconductor-insulator transition with much smaller computational efforts.
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