Detection of Berezinskii--Kosterlitz--Thouless transitions for the two-dimensional $q$-state clock models with neural networks

Autor:	Tseng, Yaun-Heng, Jiang, Fu-Jiun
Rok vydání:	2023
Předmět:	Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Statistical Mechanics High Energy Physics - Lattice
Druh dokumentu:	Working Paper
Popis:	Using the technique of supervised neural networks (NN), we study the phase transitions of two-dimensional (2D) 6- and 8-state clock models on the square lattice. The employed NN has only one input layer, one hidden layer of 2 neurons, and one output layer. In addition, the NN is trained without any prior information about the considered models. Interestingly, despite its simple architecture, the built supervised NN not only detects both the two Berezinskii--Kosterlitz--Thouless (BKT) transitions but also determines the transition temperatures with reasonable high accuracy. It is remarkable that a NN, which has an extremely simple structure and is trained without any input from the studied models, can be employed to study topological phase transitions. The outcomes shown here as well as those previously demonstrated in the literature suggest the feasibility of constructing a universal NN that is applicable to investigate the phase transitions of many systems. Comment: 6 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.00439 Zobrazit plný text záznamu View this record from Arxiv