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Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model

Author

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  • Chen, Xiangna
  • Liu, Feiyi
  • Deng, Weibing
  • Chen, Shiyang
  • Shen, Jianmin
  • Papp, Gábor
  • Li, Wei
  • Yang, Chunbin

Abstract

Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional q-state Potts model on cubic lattice by using a transfer learning approach, Domain Adversarial Neural Network (DANN). With the unique neural network architecture, it could evaluate the high-temperature (disordered) and low-temperature (ordered) phases, and identify the first and second order phase transitions. Meanwhile, by training the DANN with a few labeled configurations, the critical points for q=2,3,4 and 5 can be predicted with high accuracy, which are consistent with those of the Monte Carlo simulations. These findings would promote us to learn and explore the properties of phase transitions in high-dimensional systems.

Suggested Citation

  • Chen, Xiangna & Liu, Feiyi & Deng, Weibing & Chen, Shiyang & Shen, Jianmin & Papp, Gábor & Li, Wei & Yang, Chunbin, 2024. "Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    • Handle: RePEc:eee:phsmap:v:637:y:2024:i:c:s0378437124000414
    DOI: 10.1016/j.physa.2024.129533
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    References listed on IDEAS

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    1. Fernandes, H.A. & Arashiro, E. & Drugowich de Felício, J.R. & Caparica, A.A., 2006. "An alternative order parameter for the 4-state potts model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 255-264.
    2. Chen, Xiangna & Liu, Feiyi & Chen, Shiyang & Shen, Jianmin & Deng, Weibing & Papp, Gábor & Li, Wei & Yang, Chunbin, 2023. "Study of phase transition of Potts model with Domain Adversarial Neural Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    3. Qirui Fan & Gai Zhou & Tao Gui & Chao Lu & Alan Pak Tao Lau, 2020. "Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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