Journal
PHYSICAL REVIEW B
Volume 94, Issue 19, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.94.195105
Keywords
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Funding
- Ministry of Science and Technology of China [2016YFA0300603]
- IOP-CAS
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Unsupervised learning is a discipline of machine learning which aims at discovering patterns in large data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many-body systems. Starting with raw spin configurations of a prototypical Ising model, we use principal component analysis to extract relevant low-dimensional representations of the original data and use clustering analysis to identify distinct phases in the feature space. This approach successfully finds physical concepts such as the order parameter and structure factor to be indicators of a phase transition. We discuss the future prospects of discovering more complex phases and phase transitions using unsupervised learning techniques.
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