Abstract: The traditional description of phase transitions, originally developed by Landau, relies on the identification of a local order parameter that indicates the onset of a symmetry-breaking phase. However, many physical systems evade this paradigm and exhibit topological phase transitions where states are distinguished by their global topological properties rather than local symmetries. Using machine learning to identify such phase transitions has proven to be challenging due to their inherent non-local nature. In this talk, I discuss an unsupervised machine learning approach  that learns topological phase transitions from raw data without the need of labeling or manual feature engineering.
 Identifying topological order via unsupervised machine learning. JFRN, M. Scheurer, Nature Physics 15, 790 (2019)
The Machine Learning and Science Forum (formerly the Berkeley Statistics and Machine Learning Forum) meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. To receive email notifications about upcoming meetings, or to request more information, please contact email@example.com. All interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend.