2017/11/30 by Zhenyu Li, M. X. Luo, Mingxing Luo +1 · 30 citations
Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Condensed matter physics #Convolutional neural network #Critical exponent #Critical phenomena #Exponent #Ising model #Mathematics #Phase transition #Physics #Potts model #Quantum and electron transport phenomena #Quantum many-body systems #Scaling #Statistical physics #Theoretical and Computational Physics #cond-mat.dis-nn
paper · pdf · doi:10.1103/physrevb.99.075418
published in Physical review. B./Physical review. B 99(7) (American Physical Society) · 11 pages, 12 figures
openalex created_date 2017/12/04 · openalex publication_date 2019/02/13 · arxiv created 2019/03/16 · arxiv updated 2019/03/19 · openalex updated_date 2026/08/05
Machine learning has been successfully applied to identify phases and phase transitions in condensed matter systems. However, quantitative characterization of the critical fluctuations near phase transitions is lacking. In this paper, we propose a finite-size scaling approach based on a convolutional neural network and analyze the critical behavior of a quantum Hall plateau transition. The localization length critical exponent learned by the neural network is consistent with the value obtained by conventional approaches. We show that the general-purposed method can be used to extract critical exponents in models with drastically different physics and input data, such as the two-dimensional Ising model and four-state Potts model.