2017/03/31 by Chian-De Li, C.-D. Li, D.-R. Tan +3 · 1 citation
Materials Science · Physics and Astronomy · #Artificial neural network #Critical phenomena #Machine Learning in Materials Science #Monte Carlo method #Phase (matter) #Phase transition #Potts model #Quantum many-body systems #Simple (philosophy) #Theoretical and Computational Physics #cond-mat.dis-nn #cond-mat.stat-mech #hep-lat
paper · pdf · doi:10.1016/j.aop.2018.02.018
Revised version, 11 pages, 24 figures
arxiv created 2017/09/17 · openalex publication_date 2018/02/23 · arxiv updated 2018/04/04 · openalex created_date 2019/06/27 · openalex updated_date 2026/08/05
We study the finite temperature (FT) 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 FT 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 FT phase transitions, namely whether they are first order or second order. Our results strengthens 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.