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

TL;DR

This paper uses machine learning to analyze a Fibonacci quasicrystalline stacking of ferromagnetic layers, revealing complex magnetic phases including a unique ferromagnetic alternating helical phase with magnetization decreasing logarithmically with stack height.

Contribution

It introduces a model of magnetic heterostructures in Fibonacci quasicrystals and employs machine learning to map their complex magnetic phase diagram, discovering novel magnetic phases.

Findings

Identification of diverse magnetic phases including collinear and non-collinear states

Discovery of a unique ferromagnetic alternating helical phase

Magnetization decreases logarithmically with stack height in this phase

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.