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Time-Dependent Statistics of the Ising Model
Abstract
The individual spins of the Ising model are assumed to interact with an external agency (e.g., a heat reservoir) which causes them to change their states randomly with time. Coupling between the spins is introduced through the assumption that the transition probabilities for any one spin depend on the values of the neighboring spins. This dependence is determined, in part, by the detailed balancing condition obeyed by the equilibrium state of the model. The Markoff process which describes the spin functions is analyzed in detail for the case of a closed N-member chain. The expectation values of the individual spins and of the products of pairs of spins, each of the pair evaluated at a different time, are found explicitly. The influence of a uniform, time-varying magnetic field upon the model is discussed, and the frequency-dependent magnetic susceptibility is found in the weak-field limit. Some fluctuation-dissipation theorems are derived which relate the susceptibility to the Fourier transform of the time-dependent correlation function of the magnetization at equilibrium.
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Cites
Beitrag zur Theorie des Ferromagnetismus
Ising, E. (1925) · Zeitschrift für Physik · doi:10.1007/bf02980577 · Cited by 3,871
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Metadata
- Authors
- Glauber, R. J.
- Venue
- Journal of Mathematical Physics
- Publisher
- AIP Publishing
- Published
- 1963-02-01
- Volume
- 4
- Issue
- 2
- Pages
- 294-307
- ISSN
- 0022-2488 (ISSN-L)
- 1089-7658
- OpenAlex
- W1983272724
- Cited by
- 3,619
- References
- 1
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- closed
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- Glauber, R. J. (1963). Time-Dependent Statistics of the Ising Model. Journal of Mathematical Physics. https://doi.org/10.1063/1.1703954
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.