2022/01/31 by Alejandro José Uría-Álvarez, Daniel Molpeceres-Mingo, Uría-Álvarez, Alejandro José +4
Earth and Planetary Sciences · Physics and Astronomy · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #High-pressure geophysics and materials #Quantum many-body systems #Strongly Correlated Electrons (cond-mat.str-el) #Topological Materials and Phenomena #cond-mat.dis-nn #cond-mat.str-el
paper · pdf · doi:10.48550/arxiv.2201.13306
openalex publication_date 2022/01/31 · arxiv created 2022/02/25 · arxiv updated 2022/02/28 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
Calculation of topological invariants for crystalline systems is well understood in reciprocal space, allowing for the topological classification of a wide spectrum of materials. In this work, we present a new technique based on the entanglement spectrum, which can be used to identify the hidden topology of systems without translational invariance. By training a neural network to distinguish between trivial and topological phases using the entanglement spectrum obtained from crystalline or weakly disordered phases, we can predict the topological phase diagram for generic disordered systems. This approach becomes particularly useful for gapless systems, while providing a computational speed-up compared to the commonly used Wilson loop technique for gapful situations. Our methodology is illustrated in two-dimensional models based on the Wilson-Dirac lattice Hamiltonian.