Localization--non-ergodic transition in controllable-dimension fractal networks from diffusion-limited aggregation
Oleg I. Utesov, Alexei Andreanov, Tomasz Bednarek, Alexandra Siklitskaya, Sergei V. Koniakhin
TLDR
This paper explores spectral properties of tunable-dimension fractal networks, finding a localization-non-ergodic transition in 3D but not 2D.
Key contributions
- Investigated spectral properties of fractal agglomerates with tunable dimension using a tight-binding model.
- Observed all eigenstates are localized in 2D fractal networks.
- Discovered a localization-non-ergodic transition in 3D fractals as density increases, with critical states emerging.
- Identified compact localized states and singularities in the density of states due to complex fractal geometry.
Why it matters
This research bridges the understanding of disordered integer-dimensional systems and regular self-similar objects. It reveals how fractal geometry influences quantum localization, providing insights into material properties and quantum transport. The findings are crucial for designing materials with tailored electronic properties.
Original Abstract
Our study connects the physics of disordered integer-dimensional systems and regular self-similar objects by studying spectral properties of fractal agglomerates with tunable dimension. The latter is controlled by parameter $α$ of the algorithm that generates the agglomerates. We consider the nearest-neighbor tight-binding model on the agglomerates embedded in 2D and 3D, and observe that all eigenstates are localized in the 2D case, whereas in the 3D case, there is a localization--non-ergodic transition upon increasing $α$,i.e., going from sparse to dense fractals: a sub-extensive number of critical states emerge in the spectrum at a certain critical value of $α$. The complex geometry of the agglomerates is also responsible for a peculiar hierarchy of compact localized states and singularities in the density of states, which are typical for ordered fractals.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.