Machine learning phases and criticalities without using real data for training

D. R. Tan, F. J. Jiang*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

We study the phase transitions of three-dimensional (3D) classical O(3) model and two-dimensional (2D) classical XY model, as well as both the quantum phase transitions of 2D and 3D dimerized spin-1/2 antiferromagnets, using the technique of supervised neural network (NN). Moreover, unlike the conventional approaches commonly used in the literature, the training sets employed in our investigation are neither the theoretical nor the real configurations of the considered systems. Remarkably, with such an unconventional set up of the training stage in conjunction with some semiexperimental finite-size scaling formulas, the associated critical points determined by the NN method agree well with the established results in the literature. The outcomes obtained here imply that certain unconventional training strategies, like the one used in this study, are not only cost-effective in computation but are also applicable for a wide range of physical systems.

原文英語
文章編號224434
期刊Physical Review B
102
發行號22
DOIs
出版狀態已發佈 - 2020 十二月 28

ASJC Scopus subject areas

  • 電子、光磁材料
  • 凝聚態物理學

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