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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Machine-Learning Studies on Spin Models.

Kenta Shiina1,2, Hiroyuki Mori3, Yutaka Okabe4

  • 1Department of Physics, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan. 16879316kenta@gmail.com.

Scientific Reports
|February 9, 2020
PubMed
Summary

Machine learning classifies phases of matter by analyzing spin configurations. This study extends the method using long-range correlations for multi-component systems and Berezinskii-Kosterlitz-Thouless transitions.

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Area of Science:

  • Condensed Matter Physics
  • Statistical Mechanics
  • Machine Learning Applications

Background:

  • Conventional studies of spin models rely on thermal averages of macroscopic quantities.
  • Machine learning offers a complementary approach by classifying phases using spin configurations.

Purpose of the Study:

  • To extend and generalize machine learning methods for analyzing phase transitions in spin models.
  • To apply the technique to multi-component systems and systems with vector order parameters.
  • To analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition.

Main Methods:

  • Utilizing machine learning for phase classification.
  • Focusing on the configuration of the long-range correlation function instead of spin configurations.
  • Analyzing the Berezinskii-Kosterlitz-Thouless (BKT) transition.

Main Results:

  • Successfully classified disordered, BKT, and ordered phases.
  • Demonstrated the method's applicability to multi-component systems and systems with vector order parameters.
  • Showcased the ability to classify a model using training data from a different model.

Conclusions:

  • The generalized machine learning approach effectively classifies phases of matter, including complex systems.
  • The method provides a powerful tool for studying phase transitions beyond conventional techniques.
  • This approach opens new avenues for research in condensed matter physics and machine learning.