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Ground-state phase-space structures of two-dimensional ±J spin glasses: A network approach
Xin Cao1, Feng Wang1, Yilong Han1
1Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
We mapped spin glass phase spaces to networks, revealing Gaussian distributions and community structures. These complex networks exhibit fractal properties and change significantly at phase transitions, offering new insights into spin glass systems.
Area of Science:
- Condensed Matter Physics
- Statistical Mechanics
- Network Science
Background:
- Spin glasses are complex magnetic materials with disordered interactions.
- Understanding their ground-state phase space is crucial for characterizing their behavior.
- Previous studies often relied on simulations or limited analytical methods.
Purpose of the Study:
- To develop and apply a complex-network approach for analyzing spin glass phase spaces.
- To uncover fundamental properties of spin glass ground states and their dynamics.
- To investigate the topological characteristics of spin glass phase space networks.
Main Methods:
- Exact mapping of ground-state phase spaces of 2D Edwards-Anderson bimodal spin glasses to networks.
- Analysis of network properties including connectivity distribution, spectra, community structures, and fractal dimensions.
- Construction of a coarse-grained network representing the entropy landscape.
Main Results:
- Phase-space networks exhibit Gaussian connectivity distributions and spectra, indicative of specific microstate visiting frequencies.
- Discovery of community structures within phase-space networks, leading to a scale-free entropy landscape network.
- Phase-space networks demonstrate fractal structures, linked to the rugged entropy landscape.
- Significant changes in network properties (connectivity, community, fractal) observed at the ferromagnetic-to-glass phase transition.
Conclusions:
- The complex-network approach provides quantitative insights into spin glass ground states.
- Spin glass phase-space networks form a distinct class of complex networks with unique topology.
- These findings offer a novel perspective on the statistical mechanics of disordered systems.
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