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Main Authors: Li, Hongze, Orbach, Raymond L., Kenning, Gregory G.
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2604.13480
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author Li, Hongze
Orbach, Raymond L.
Kenning, Gregory G.
author_facet Li, Hongze
Orbach, Raymond L.
Kenning, Gregory G.
contents Spin glass dynamics is a strong function of spatial dimensionality $D$. The lower critical dimension is close to 2.5, so that, in two dimensions, the condensation temperature $T_\text{g}=0$, and only fluctuations are present at finite temperatures. However, by using thin film multilayers, one can explore the dynamics in both $D=3$ and $D=2$ dimensions. Spin glass thin film multilayers transition from $D=3$ dynamics at short to intermediate times to $D = 2$ dynamics at long times. Correlation lengths of CuMn 4.5 nm multilayers at long times are shown to be grow more rapidly in $D=2$ as compared to $D=3$, and for the longest measurement time, experimentally reach equilibrium in qualitative agreement with simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2604_13480
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Dynamics of spin glasses in two dimensions
Li, Hongze
Orbach, Raymond L.
Kenning, Gregory G.
Disordered Systems and Neural Networks
Spin glass dynamics is a strong function of spatial dimensionality $D$. The lower critical dimension is close to 2.5, so that, in two dimensions, the condensation temperature $T_\text{g}=0$, and only fluctuations are present at finite temperatures. However, by using thin film multilayers, one can explore the dynamics in both $D=3$ and $D=2$ dimensions. Spin glass thin film multilayers transition from $D=3$ dynamics at short to intermediate times to $D = 2$ dynamics at long times. Correlation lengths of CuMn 4.5 nm multilayers at long times are shown to be grow more rapidly in $D=2$ as compared to $D=3$, and for the longest measurement time, experimentally reach equilibrium in qualitative agreement with simulations.
title Dynamics of spin glasses in two dimensions
topic Disordered Systems and Neural Networks
url https://arxiv.org/abs/2604.13480