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Title: A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models
Authors: Richter-Laskowska, Monika
Khan, H.
Trivedi, N.
Maśka, Maciej M.
Keywords: phase transitions; topological defects; XY model; arti1cial neural networks; machine learning
Issue Date: 2018
Citation: Condensed Matter Physics, Vol. 21, No 3 (2018), Art. No. 33602
Abstract: The Berezinskii-Kosterlitz-Thouless transition is a very speci1c phase transition where all thermodynamic quantities are smooth. Therefore, it is diWcult to determine the critical temperature in a precise way. In this paper we demonstrate how neural networks can be used to perform this task. In particular, we study how the accuracy of the transition identi1cation depends on the way the neural networks are trained. We apply our approach to three different systems: (i) the classical XY model, (ii) the phase-fermion model, where classical and quantum degrees of freedom are coupled and (iii) the quantum XY model.
DOI: 10.5488/CMP.21.33602
ISSN: 1607-324X
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