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.
Speaker: Joaquin Rodriguez Nieva, Stanford
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