Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning

• URL: http://arxiv.org/abs/2307.16052v1
• Date: Sat, 29 Jul 2023 19:03:12 GMT
• Title: Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning
• Authors: Pablo S. Cornaglia, Matias Nu\~nez, D. J. Garcia
• Abstract summary: We study a Fibonacci quasicrystalline stacking of ferromagnetic layers, potentially realizable using van der Waals magnetic materials. We construct a model of this magnetic heterostructure, that displays a complex relationship between geometric frustration and magnetic order in this quasicrystalline system. We employ a machine learning approach, which proves to be a powerful tool in revealing the complex magnetic behavior of this system.
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• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we conduct a comprehensive theoretical analysis of a Fibonacci quasicrystalline stacking of ferromagnetic layers, potentially realizable using van der Waals magnetic materials. We construct a model of this magnetic heterostructure, which includes up to second neighbor interlayer magnetic interactions, that displays a complex relationship between geometric frustration and magnetic order in this quasicrystalline system. To navigate the parameter space and identify distinct magnetic phases, we employ a machine learning approach, which proves to be a powerful tool in revealing the complex magnetic behavior of this system. We offer a thorough description of the magnetic phase diagram as a function of the model parameters. Notably, we discover among other collinear and non-collinear phases, a unique ferromagnetic alternating helical phase. In this non-collinear quasiperiodic ferromagnetic configuration the magnetization decreases logarithmically with the stack height.

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