Anderson localization induced by structural disorder
- URL: http://arxiv.org/abs/2411.10247v1
- Date: Fri, 15 Nov 2024 14:58:10 GMT
- Title: Anderson localization induced by structural disorder
- Authors: Sourav Bhattacharjee, Piotr Sierant, Marek DudyĆski, Jan Wehr, Jakub Zakrzewski, Maciej Lewenstein,
- Abstract summary: We show that the Anderson localization transition occurs when the strength of the structural disorder is smoothly increased.
Our work identifies a new class of structurally disordered lattice models in which destructive interference of matter waves may inhibit transport and lead to a transition between metallic and localized phases.
- Score: 0.0
- License:
- Abstract: We examine the onset of Anderson localization in three-dimensional systems with structural disorder in the form of lattice irregularities and in the absence of any on-site disordered potential. Analyzing two models with distinct types of lattice regularities, we show that the Anderson localization transition occurs when the strength of the structural disorder is smoothly increased. Performing finite-size scaling analysis of the results, we show that the transition belongs to the same universality class as regular Anderson localization induced by onsite disorder. Our work identifies a new class of structurally disordered lattice models in which destructive interference of matter waves may inhibit transport and lead to a transition between metallic and localized phases.
Related papers
- Resonances, mobility edges and gap-protected Anderson localization in generalized disordered mosaic lattices [0.0]
We introduce a broader class of mosaic lattices and derive expressions of mobility edges and localization length for incommensurate sinusoidal disorder.
For both incommensurate and uncorrelated disorder, we prove that Anderson localization is protected by the open gaps of the disorder-free lattice.
arXiv Detail & Related papers (2024-10-25T12:43:17Z) - Relative Representations: Topological and Geometric Perspectives [53.88896255693922]
Relative representations are an established approach to zero-shot model stitching.
We introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations.
Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes.
arXiv Detail & Related papers (2024-09-17T08:09:22Z) - Compositional Structures in Neural Embedding and Interaction Decompositions [101.40245125955306]
We describe a basic correspondence between linear algebraic structures within vector embeddings in artificial neural networks.
We introduce a characterization of compositional structures in terms of "interaction decompositions"
We establish necessary and sufficient conditions for the presence of such structures within the representations of a model.
arXiv Detail & Related papers (2024-07-12T02:39:50Z) - Isospectrally Patterned Aperiodic Lattices [0.0]
We design and explore patterned aperiodic lattices consisting of coupled isospectral cells that vary across the lattice.
The characteristic localization length emerges due to a competition of the involved phase gradient and the coupling between the cells.
The fraction of localized versus delocalized eigenstates can be tuned by changing the gradient between the cells of the lattice.
arXiv Detail & Related papers (2024-06-26T15:28:58Z) - Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints [0.0]
This study employs Isomap, a manifold learning technique, to unveil the intrinsic structure of high-dimensional density field data of dislocation structures.
The resulting maps provide a systematic framework for quantitatively comparing dislocation structures, offering unique fingerprints based on density fields.
arXiv Detail & Related papers (2024-06-21T09:32:09Z) - Score-based Causal Representation Learning with Interventions [54.735484409244386]
This paper studies the causal representation learning problem when latent causal variables are observed indirectly.
The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables.
arXiv Detail & Related papers (2023-01-19T18:39:48Z) - Robust Self-Supervised LiDAR Odometry via Representative Structure
Discovery and 3D Inherent Error Modeling [67.75095378830694]
We develop a two-stage odometry estimation network, where we obtain the ego-motion by estimating a set of sub-region transformations.
In this paper, we aim to alleviate the influence of unreliable structures in training, inference and mapping phases.
Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors.
arXiv Detail & Related papers (2022-02-27T12:52:27Z) - Topological transitions and Anderson localization of light in disordered
atomic arrays [0.0]
We study the interplay of disorder and topological phenomena in honeycomb lattices of atoms coupled by the electromagnetic field.
On the one hand, disorder can trigger insulator transitions between distinct topological phases and drive the lattice into the topological Anderson state.
We find that disorder can both open a topological pseudogap in the spectrum of an otherwise topologically trivial system and introduce spatially localized modes inside it.
arXiv Detail & Related papers (2021-12-29T17:44:02Z) - Towards Robust and Adaptive Motion Forecasting: A Causal Representation
Perspective [72.55093886515824]
We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables.
We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph.
Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
arXiv Detail & Related papers (2021-11-29T18:59:09Z) - Localisation in quasiperiodic chains: a theory based on convergence of
local propagators [68.8204255655161]
We present a theory of localisation in quasiperiodic chains with nearest-neighbour hoppings, based on the convergence of local propagators.
Analysing the convergence of these continued fractions, localisation or its absence can be determined, yielding in turn the critical points and mobility edges.
Results are exemplified by analysing the theory for three quasiperiodic models covering a range of behaviour.
arXiv Detail & Related papers (2021-02-18T16:19:52Z) - Perturbative instability towards delocalization at phase transitions
between MBL phases [0.0]
We find evidence for a perturbative instability of localization at finite energy densities once interactions are added.
We introduce a novel diagnostic, the "susceptibility of entanglement", which allows us to perturbatively probe the effect of adding interactions on the entanglement properties of eigenstates.
arXiv Detail & Related papers (2020-08-20T17:59:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.