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Deep machine learning investigates first-order phase transitions. A novel protocol classifies spin configurations, estimating phase probabilities and critical energies for the Potts model.

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Area of Science:

  • Statistical Mechanics
  • Machine Learning
  • Computational Physics

Background:

  • Investigating critical phenomena in systems with first-order phase transitions is computationally challenging.
  • Traditional methods struggle to accurately characterize the complex behavior near phase transitions.

Purpose of the Study:

  • To explore the application of deep machine learning for analyzing critical behavior in first-order phase transitions.
  • To develop a machine learning protocol for classifying system phases and estimating transition properties.

Main Methods:

  • A machine learning protocol using ternary classification of instantaneous spin configurations.
  • Training a neural network on known disordered and ordered phase energies.
  • Utilizing the trained network to predict phase probabilities for given energies.

Main Results:

  • Successfully estimated critical energies and latent heat for the Potts model (10 and 20 components).
  • Provided the first estimation of probabilities for configurations belonging to ordered, coexistence, and disordered phases.
  • Observed that phase probabilities may indicate geometric transitions within the coexistence phase.

Conclusions:

  • Deep machine learning offers a powerful new approach for studying critical phenomena in first-order phase transitions.
  • The proposed protocol accurately characterizes phase behavior and quantifies transition parameters.
  • The findings open avenues for exploring geometric transitions using machine learning in phase coexistence regions.