Machine Learning Phase Transition: An Iterative Proposal

Abstract

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. Newly labelled samples would be put in the training set used in subsequent training and the phase boundaries would be updated as well. The average of the phase boundaries is expected to converge to the critical temperature in this proposal. In concrete examples, we implement this proposal to estimate the critical temperatures for two q-state Potts models with continuous and first order phase transitions. Linear and manifold dimensionality-reduction techniques are employed in the first step. Both a convolutional neural network and a bidirectional recurrent neural network with long short-term memory units perform well for two Potts models in the second step. The convergent behaviors of the estimations reflect the types of phase transitions. And the results indicate that our proposal may be used to explore phase transitions for new general statistical models.