Discovering Phase Transitions with Unsupervised Learning

Understand

Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly.

• We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many body systems.
• Starting with raw spin configurations of a prototypical Ising model, we use principal component analysis to extract relevant low dimensional representations the original data and use clustering analysis to identify distinct phases in the feature space.
• This approach successfully finds out physical concepts such as order parameter and structure factor to be indicators of the phase transition.