Applications of neural networks to the studies of phase transitions of two-dimensional Potts models

C. D. Li, D. R. Tan, F. J. Jiang

Research output: Contribution to journalArticle

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Abstract

We study the phase transitions of two-dimensional (2D) Q-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We demonstrate that the ideas from NN can be adopted to study these considered phase transitions efficiently. In particular, even with a simple NN constructed in this investigation, we are able to obtain the relevant information of the nature of these phase transitions, namely whether they are first order or second order. Our results strengthen the potential applicability of machine learning in studying various states of matters. Subtlety of applying NN techniques to investigate many-body systems is briefly discussed as well.

LanguageEnglish
Pages312-331
Number of pages20
JournalAnnals of Physics
Volume391
DOIs
Publication statusPublished - 2018 Apr 1

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two dimensional models
machine learning
simulation

Keywords

  • Monte Carlo simulations
  • Phase transition
  • Supervised neural networks

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Applications of neural networks to the studies of phase transitions of two-dimensional Potts models. / Li, C. D.; Tan, D. R.; Jiang, F. J.

In: Annals of Physics, Vol. 391, 01.04.2018, p. 312-331.

Research output: Contribution to journalArticle

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