Learning phase transitions by confusion

@article{Nieuwenburg2017LearningPT,
  title={Learning phase transitions by confusion},
  author={Evert van Nieuwenburg and Ye-Hua Liu and Sebastian D. Huber},
  journal={Nature Physics},
  year={2017},
  volume={13},
  pages={435-439}
}
A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list. Classifying phases of matter is key to our understanding of many problems in physics. For quantum-mechanical systems in particular, the task can be daunting due to the exponentially large Hilbert space. With modern computing power and access to ever… 
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