Predicting topological invariants and unconventional superconducting pairing from density of states and machine learning

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dc.contributor.authorSantos, Flávio Luis Noronha dos
dc.contributor.authorCanabarro, Askery
dc.contributor.authorAraújo, Rafael Chaves Souto
dc.contributor.authorPereira, Rodrigo G.
dc.date.accessioned2025-05-27T16:53:25Z
dc.date.available2025-05-27T16:53:25Z
dc.date.issued2025-01-02
dc.description.resumoCompetition between magnetism and superconductivity can lead to unconventional and topological superconductivity. However, the experimental confirmation of the presence of Majorana edge states and unconventional pairing poses a major challenge. Here, we consider a two-dimensional lattice model for a superconductor with spin-orbit coupling and exchange coupling to randomly distributed magnetic impurities. Depending on parameters of the model, this system may display topologically trivial or nontrivial edge states. We map out the phase diagram by computing the Bott index, a topological invariant defined in real space. We then use machine learning (ML) algorithms to predict the Bott index from the local density of states (LDOS) at zero energy, obtaining high-accuracy results. We also train ML models to predict the amplitude of odd-frequency pairing in the anomalous Green's function at zero energy. Once the ML models are trained using the LDOS, which is experimentally accessible via scanning tunneling spectroscopy, our method could be applied to predict the number of Majorana edge states and estimate the magnitude of odd-frequency pairing in real materials.pt_BR
dc.identifier.citationSANTOS, Flávio Luis Noronha dos; CANABARRO, Askery; ARAÚJO, Rafael Chaves Souto; PEREIRA, Rodrigo G. Predicting topological invariants and unconventional superconducting pairing from density of states and machine learning. Physical Review B, v. 111, p. 014501, 2025. DOI 10.1103/PhysRevB.111.014501. Disponível em: https://journals.aps.org/prb/abstract/10.1103/PhysRevB.111.014501. Acesso em: 17 mar. 2025.
dc.identifier.doihttps://doi.org/10.1103/PhysRevB.111.014501
dc.identifier.issn2469-9969
dc.identifier.otherhttps://doi.org/10.1103/PhysRevB.111.014501
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/63674
dc.languageenpt_BR
dc.language.isoen
dc.publisherPhysical Review Bpt_BR
dc.subjectOdd-frequency superconductivitypt_BR
dc.subjectSupercondutividade de frequência ímparpt_BR
dc.subjectTopological superconductorspt_BR
dc.subjectSupercondutores topológicospt_BR
dc.subjectDisordered systemspt_BR
dc.subjectSistemas desordenadospt_BR
dc.subjectMachine learningpt_BR
dc.subjectAprendizado de máquinapt_BR
dc.titlePredicting topological invariants and unconventional superconducting pairing from density of states and machine learningpt_BR
dc.typearticlept_BR

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