Identifying topological order through unsupervised machine learning

@article{RodriguezNieva2018IdentifyingTO,
  title={Identifying topological order through unsupervised machine learning},
  author={J. Rodriguez-Nieva and M. S. Scheurer},
  journal={Nature Physics},
  year={2018},
  pages={1-6}
}
The Landau description of phase transitions relies on the identification of a local order parameter that indicates the onset of a symmetry-breaking phase. In contrast, topological phase transitions evade this paradigm and, as a result, are harder to identify. Recently, machine learning techniques have been shown to be capable of characterizing topological order in the presence of human supervision. Here, we propose an unsupervised approach based on diffusion maps that learns topological phase… Expand
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