Unveiling exotic magnetic phases in Fibonacci quasicrystals through machine learning

In this study, we present a comprehensive theoretical analysis of magnetic Fibonacci quasicrystals, which could potentially be realized through the stacking of ferromagnetic van der Waals layers. We introduce a model that incorporates up to second-neighbor interlayer magnetic interactions and displays a complex interplay between geometric frustration and magnetic order. To explore the parameter space and identify distinct magnetic phases, we employ a machine learning approach. This methodology proves effective in elucidating the intricate magnetic behavior of the system. We offer a detailed magnetic phase diagram as a function of the model parameters and notably discover a unique ferromagnetic alternating helical phase among other collinear and noncollinear phases. In this noncollinear, quasiperiodic, and ferromagnetic configuration, the magnetization decreases logarithmically with the stack height.