Principal component analysis of the magnetic transition in the three-dimensional Fermi-Hubbard model
Document Type
Article
Publication Date
January 2019
Publication Title
Journal of Physics: Conference Series
Volume
1290
Issue Number
1
DOI
10.1088/1742-6596/1290/1/012006
Disciplines
Astrophysics and Astronomy | Physical Sciences and Mathematics | Physics
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.
Recommended Citation
Ehsan Khatami. "Principal component analysis of the magnetic transition in the three-dimensional Fermi-Hubbard model" Journal of Physics: Conference Series (2019). https://doi.org/10.1088/1742-6596/1290/1/012006
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
This article was published in Journal of Physics: Conference Series, volume 1290, issue 1, 2019 and can also be found at this link.