Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning
This work addresses the challenge of understanding exotic magnetic behaviors in quasicrystalline systems for researchers in condensed matter physics, but it is incremental as it applies existing machine learning methods to a new magnetic model.
The study tackled the problem of identifying magnetic phases in a Fibonacci quasicrystalline stacking of ferromagnetic layers by constructing a model with interlayer interactions and using machine learning to navigate the parameter space, resulting in the discovery of a unique ferromagnetic alternating helical phase where magnetization decreases logarithmically with stack height.
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.