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Title: Unsupervised data-driven classification of topological gapped systems with symmetries
Authors: Long, Yang
Zhang, Baile
Keywords: Science::Physics
Issue Date: 2023
Source: Long, Y. & Zhang, B. (2023). Unsupervised data-driven classification of topological gapped systems with symmetries. Physical Review Letters, 130(3), 036601-.
Project: NRF-CRP23-2019-0007
Journal: Physical Review Letters
Abstract: A remarkable breakthrough in topological phase classification is the establishment of the topological periodic table, which is mainly based on the classifying space analysis or K theory, but not based on concrete Hamiltonians that possess finite bands or arise in a lattice. As a result, it is still difficult to identify the topological phase of an arbitrary Hamiltonian; the common practice is, instead, to check the incomplete and still growing list of topological invariants one by one, very often by trial and error. Here, we develop unsupervised classifications of topological gapped systems with symmetries, and demonstrate the data-driven construction of the topological periodic table without a priori knowledge of topological invariants. This unsupervised data-driven strategy can take into account spatial symmetries, and further classify phases that were previously classified as trivial in the past. Our Letter introduces machine learning into topological phase classification and paves the way for intelligent explorations of new phases of topological matter.
ISSN: 0031-9007
DOI: 10.1103/PhysRevLett.130.036601
Schools: School of Physical and Mathematical Sciences 
Departments: Division of Physics and Applied Physics
Research Centres: Centre for Disruptive Photonic Technologies (CDPT) 
Rights: © 2023 American Physical Society. All rights reserved. This paper was published in Physical Review Letters and is made available with permission of American Physical Society.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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