  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.
