TY - JOUR
T1 - Neural network evidence of a weakly first-order phase transition for the two-dimensional 5-state Potts model
AU - Tseng, Yuan Heng
AU - Tseng, Yun Hsuan
AU - Jiang, Fu Jiun
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/12
Y1 - 2022/12
N2 - A universal (supervised) neural network (NN), which is trained only once on a one-dimensional lattice of 200 sites, is employed to study the phase transition of the two-dimensional (2D) 5-state ferromagnetic Potts model on the square lattice. In particular, the NN is obtained by using two artificially made configurations as the training set. Due to the unique features of the employed NN, results associated with systems consisting of over 4,000,000 spins can be obtained with ease, and convincing NN evidence showing that the investigated phase transition is weakly first order is reached.
AB - A universal (supervised) neural network (NN), which is trained only once on a one-dimensional lattice of 200 sites, is employed to study the phase transition of the two-dimensional (2D) 5-state ferromagnetic Potts model on the square lattice. In particular, the NN is obtained by using two artificially made configurations as the training set. Due to the unique features of the employed NN, results associated with systems consisting of over 4,000,000 spins can be obtained with ease, and convincing NN evidence showing that the investigated phase transition is weakly first order is reached.
UR - http://www.scopus.com/inward/record.url?scp=85144863803&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144863803&partnerID=8YFLogxK
U2 - 10.1140/epjp/s13360-022-03597-4
DO - 10.1140/epjp/s13360-022-03597-4
M3 - Article
AN - SCOPUS:85144863803
SN - 2190-5444
VL - 137
JO - European Physical Journal Plus
JF - European Physical Journal Plus
IS - 12
M1 - 1374
ER -