Machine Learning Phase Transition: An Iterative Proposal

Understand

We propose an iterative proposal to estimate critical points for statistical models based on configurations by combing machine-learning tools.

• Firstly, phase scenarios and preliminary boundaries of phases are obtained by dimensionality-reduction techniques.
• Besides, this step not only provides labelled samples for the subsequent step but also is necessary for its application to novel statistical models.
• Secondly, making use of these samples as training set, neural networks are employed to assign labels to those samples between the phase boundaries in an iterative manner.