The Physics Society of Iran Iranian Journal of Physics Research 1682-6957 22 2 2022 08 23 Phase diagram of the Heisenberg model: machine learning method ‎ Phase diagram of the Heisenberg model: machine learning method ‎ 373 385 3254 10.47176/ijpr.22.2.01344 FA Mohammad-Hossein Zare Qom University of Technology 0000-0002-4670-7250 Abdolreza Rasouli Kenari Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom 37181-46645, Iran Journal Article 2021 10 31 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. 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. https://ijpr.iut.ac.ir/article_3254_8deb8d1dd92840f975b6931ab3a3c61e.pdf