English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent years, developing unsupervised machine learning for identifyingphase transition is a research direction. In this paper, we introduce atwo-times clustering method that can help select perfect configurations from aset of degenerate samples and assign the configuration with labels in a mannerof unsupervised machine learning. These perfect configurations can then be usedto train a neural network to classify phases. The derivatives of the predictedclassification in the phase diagram, show peaks at the phase transition points.The effectiveness of our method is tested for the Ising, Potts, and Blume-Capelmodels. By using the ordered configuration from two-times clustering, ourmethod can provide a useful way to obtain phase diagrams.

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