Automatic learning of topological phase boundaries
نویسندگان
چکیده
Topological phase transitions, which do not adhere to Landau's phenomenological model (i.e., a spontaneous symmetry breaking process and vanishing local order parameters), have been actively researched in condensed matter physics. Machine learning of topological transitions has generally proved difficult due the global nature indices. Only recently method diffusion maps shown be effective at identifying changes order. However, previous map results required adjustments two hyperparameters: data length scale number boundaries. In this article we introduce heuristic that requires no such tuning. This allows computer programs locate appropriate hyperparameters without user input. We demonstrate method's efficacy by drawing remarkably accurate diagrams three physical models: Haldane graphene, generalization Su-Schreiffer-Haeger model, for quantum ring with tunnel junctions. These are drawn, human intervention, from supplied range parameters.
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ژورنال
عنوان ژورنال: Physical Review E
سال: 2021
ISSN: ['1550-2376', '1539-3755']
DOI: https://doi.org/10.1103/physreve.103.023310