Chinese Physics C > 2025, Vol. 49 > Issue(5): 054103 DOI: 10.1088/1674-1137/adaa59

Exploring percolation phase transition in the three-dimensional Ising model with machine learning

1.
Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, Wuhan 430079, China
2.
Department of Physics, Swansea University, SA2 8PP, Swansea, United Kingdom
3.
College of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan 442000, China

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Abstract：
Studying percolation phase transitions offers valuable insights into the characteristics of phase transitions, shedding light on the underlying mechanisms that govern the formation of global connectivity within a system. We explore the percolation phase transition in the 3D cubic Ising model by employing two machine learning techniques. Our results demonstrate that machine learning methods can distinguish different phases during the percolation transition. Through the finite-size scaling analysis on the output of the neural networks, the percolation temperature and a correlation length exponent in the geometrical percolation transition are extracted and compared to those in the thermal magnetization phase transition within the 3D Ising model. These findings provide a valuable method for enhancing our understanding of the properties of the QCD critical point, which belongs to the same universality class as the 3D Ising model.
- percolation phase transition,
- machine learning,
- QCD critical point,
- three-dimensional Ising model

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Exploring percolation phase transition in the three-dimensional Ising model with machine learning

Abstract：

References

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