Unsupervised identification of topological order using predictive models

Understand

Machine-learning driven models have proven to be powerful tools for the identification of phases of matter.

• In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior theoretical knowledge.
• While for phases characterized by a broken symmetry, the use of unsupervised methods has proven to be successful, topological phases without a local order parameter seem to be much harder to identify without supervision.
• Here, we use an unsupervised approach to identify topological phases and transitions out of them.