Phase diagram of the Heisenberg model: machine learning method ‎

Zare, Mohammad-Hossein; Rasouli Kenari, Abdolreza

doi:10.47176/ijpr.22.2.01344

Phase diagram of the Heisenberg model: machine learning method ‎

Document Type : Original Article

Authors

Mohammad-Hossein Zare ¹

Abdolreza Rasouli Kenari ²

¹ Qom University of Technology

² Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom 37181-46645, Iran

https://doi.org/10.47176/ijpr.22.2.01344

Abstract

Machine learning, as one of the most powerful tools, has provided an unprecedented perspective on the study of classifying different phases and phase transitions between them in condensed matter physics. Here, we employed unsupervised machine learning algorithms to investigate magnetic ground states for systems of spontaneous symmetry breaking below the Curie temperature. In this study, we investigate the classical phase diagram of the Heisenberg model on square and honeycomb lattices using the deep machine learning algorithm. In the classical treatment, our findings show a good agreement with the classical phase of the Heisenberg model obtained by means of other conventional methods.

Keywords

machine learning

deep neutral lattice

adam optimizer

Heisenberg model

20.1001.1.16826957.1401.22.2.7.9

Subjects

Condensed Matter Physics

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