Machine learning puts a new spin on spin models

Researchers from Tokyo Metropolitan University have used machine learning to analyze spin models, which are used in physics to study phase transitions. Previous work showed that an image/handwriting classification model could be applied to distinguish states in the simplest models. The team showed the approach is applicable to more complex models and found that an AI trained on one model and applied to another could reveal key similarities between distinct phases in different systems.

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