Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking
  of Ferromagnetic Layers through Machine Learning
Pablo S. Cornaglia Matias Nuñez D. J. Garcia
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.