A simple framework for contrastive learning phases of matter
This provides a flexible, generic tool for physicists to identify phase transitions across diverse scenarios, though it appears incremental as it adapts existing contrastive learning methods to this domain.
The authors tackled the problem of recognizing and classifying phases of matter in condensed-matter physics by introducing SimCLP, a simple contrastive learning framework that successfully applies to various classical, quantum, and topological systems without manual feature engineering.
A main task in condensed-matter physics is to recognize, classify, and characterize phases of matter and the corresponding phase transitions, for which machine learning provides a new class of research tools due to the remarkable development in computing power and algorithms. Despite much exploration in this new field, usually different methods and techniques are needed for different scenarios. Here, we present SimCLP: a simple framework for contrastive learning phases of matter, which is inspired by the recent development in contrastive learning of visual representations. We demonstrate the success of this framework on several representative systems, including classical and quantum, single-particle and many-body, conventional and topological. SimCLP is flexible and free of usual burdens such as manual feature engineering and prior knowledge. The only prerequisite is to prepare enough state configurations. Furthermore, it can generate representation vectors and labels and hence help tackle other problems. SimCLP therefore paves an alternative way to the development of a generic tool for identifying unexplored phase transitions.