# Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination.

@article{Hu2017DiscoveringPP,
title={Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination.},
author={Wenjian Hu and Rajiv R. P. Singh and Richard T. Scalettar},
journal={Physical review. E},
year={2017},
volume={95 6-1},
pages={
062122
}
}
• Published 31 March 2017
• Physics
• Physical review. E
We apply unsupervised machine learning techniques, mainly principal component analysis (PCA), to compare and contrast the phase behavior and phase transitions in several classical spin models-the square- and triangular-lattice Ising models, the Blume-Capel model, a highly degenerate biquadratic-exchange spin-1 Ising (BSI) model, and the two-dimensional XY model-and we examine critically what machine learning is teaching us. We find that quantified principal components from PCA not only allow…
178 Citations

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