Machine Learning the Square-Lattice Ising Model

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.

6 pages, 5 figures, submitted to the Proceedings for XXXII IUPAP Conference on Computational Physics (2021)

References in corpus (4)

• Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
• Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region with a Variational Autoencoder
• Phase diagram study of a two-dimensional frustrated antiferromagnet via unsupervised machine learning
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Cited by in corpus (3)

• Machine Learning of Nonequilibrium Phase Transition in an Ising Model on Square Lattice
• Machine learning of phases and structures for model systems in physics
• Phase determination with and without deep learning