Unsupervised machine learning for identifying phase transition using two-times clustering

Autor:	Wu, Nan, Li, Zhuohan, Zhang, Wanzhou
Rok vydání:	2023
Předmět:	Condensed Matter - Disordered Systems and Neural Networks
Druh dokumentu:	Working Paper
Popis:	In recent years, developing unsupervised machine learning for identifying phase transition is a research direction. In this paper, we introduce a two-times clustering method that can help select perfect configurations from a set of degenerate samples and assign the configuration with labels in a manner of unsupervised machine learning. These perfect configurations can then be used to train a neural network to classify phases. The derivatives of the predicted classification in the phase diagram, show peaks at the phase transition points. The effectiveness of our method is tested for the Ising, Potts, and Blume-Capel models. By using the ordered configuration from two-times clustering, our method can provide a useful way to obtain phase diagrams. Comment: 8 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.17687 Zobrazit plný text záznamu View this record from Arxiv