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Machine learning the square-lattice Ising model
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Çivitcioğlu, Burak, Roemer, Rudolf A. and Honecker, Andreas (2022) Machine learning the square-lattice Ising model. Journal of Physics: Conference Series, 2207 (1). 012058. doi:10.1088/1742-6596/2207/1/012058 ISSN 1742-6596.
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Official URL: https://doi.org/10.1088/1742-6596/2207/1/012058
Abstract
Recently, machine-learning methods have been shown to be successful in identifying and classifying different phases of the square-lattice Ising model. We study the performance and limits of classification and regression models. In particular, we investigate how accurately the correlation length, energy and magnetisation can be recovered from a given configuration. We find that a supervised learning study of a regression model yields good predictions for magnetisation and energy, and acceptable predictions for the correlation length.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > Q Science (General) Q Science > QC Physics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||
SWORD Depositor: | Library Publications Router | ||||
Library of Congress Subject Headings (LCSH): | Machine learning, Condensed matter -- Computer simulation, Condensed matter -- Mathematical models, Monte Carlo method, Ising model, Phase transformations (Statistical physics) | ||||
Journal or Publication Title: | Journal of Physics: Conference Series | ||||
Publisher: | Institute of Physics Publishing Ltd. | ||||
ISSN: | 1742-6596 | ||||
Official Date: | 1 March 2022 | ||||
Dates: |
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Volume: | 2207 | ||||
Number: | 1 | ||||
Article Number: | 012058 | ||||
DOI: | 10.1088/1742-6596/2207/1/012058 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 23 May 2022 | ||||
Date of first compliant Open Access: | 24 May 2022 |
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