Document Type

Article

Publication Date

January 2019

Abstract

Machine learning techniques have been widely used in the study of strongly correlated systems in recent years. Here, we review some applications to classical and quantum many-body systems and present results from an unsupervised machine learning technique, the principal component analysis, employed to identify the finite-temperature phase transition of the three-dimensional Fermi-Hubbard model to the antiferromagnetically ordered state. We find that this linear method can capture the phase transition as well as other more complicated and nonlinear counterparts.

Comments

This article was published in Journal of Physics: Conference Series, volume 1290, issue 1, 2019 and can also be found at this link.

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