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.

通过理论分析 Fibonacci 准晶层叠的铁磁层，本研究构建了一个该磁异质结构的模型，并在此准晶系统中展示了几何阻碍与磁序之间的复杂关系。为了确定不同的磁相，我们采用了机器学习方法，其证明在揭示该系统的复杂磁性行为方面是有效的。我们详细描述了模型参数对应的磁相图，并发现了独特的铁磁交替螺旋相，其中磁化随层高以对数方式减小。