Assessing the structural heterogeneity of supercooled liquids through community inference.

@article{Paret2020AssessingTS,
  title={Assessing the structural heterogeneity of supercooled liquids through community inference.},
  author={Joris Paret and Robert L. Jack and Daniele Coslovich},
  journal={The Journal of chemical physics},
  year={2020},
  volume={152 14},
  pages={
          144502
        }
}
We present an information-theoretic approach inspired by distributional clustering to assess the structural heterogeneity of particulate systems. Our method identifies communities of particles that share a similar local structure by harvesting the information hidden in the spatial variation of two- or three-body static correlations. This corresponds to an unsupervised machine learning approach that infers communities solely from the particle positions and their species. We apply this method to… 

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