Open AccessPrincipal component analysis of the magnetic transition in the three-dimensional Fermi-Hubbard model
Author(s) -
Ehsan Khatami
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1290/1/012006
Subject(s) - hubbard model , principal component analysis , fermi gamma ray space telescope , phase transition , nonlinear system , component (thermodynamics) , statistical physics , physics , principal (computer security) , quantum , condensed matter physics , computer science , quantum mechanics , artificial intelligence , superconductivity , operating system
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.