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Vitrification

Machines learn to recognize glasses

Nature Physics volume 12, pages 377–378 (2016)Cite this article

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The dynamics of a viscous liquid undergo a dramatic slowdown when it is cooled to form a solid glass. Recognizing the structural changes across such a transition remains a major challenge. Machine-learning methods, similar to those Facebook uses to recognize groups of friends, have now been applied to this problem.

The difficulty in recognizing any structural fingerprints of the glass transition is particularly frustrating when it comes to computational studies of vitrification, as these have, in principle, direct access to the full structural and dynamical behaviour of individual material components. Confronted with the challenge of finding non-obvious patterns within large amounts of information, materials scientists are resorting to modern machine-learning techniques that can sift through and classify big data3. For example, dimensionality-reduction algorithms yield navigation maps of collective rearrangements in protein folding4 and polymeric transitions5, and clustering algorithms identify groups of related structures6, much like how Facebook recognizes groups of friends within its social network.

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Figure 1: A glass maker uses shears to separate the molten glass from the final object.

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Authors and Affiliations

  1. Michele Ceriotti is at the Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland,

    Michele Ceriotti

  2. Vincenzo Vitelli is at the Instituut-Lorentz for Theoretical Physics, Leiden University, Niels Bohrweg 2, Leiden, The Netherlands,

    Vincenzo Vitelli

Authors
  1. Michele Ceriotti
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  2. Vincenzo Vitelli
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Correspondence to Michele Ceriotti or Vincenzo Vitelli.

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Ceriotti, M., Vitelli, V. Machines learn to recognize glasses. Nature Phys 12, 377–378 (2016). https://doi.org/10.1038/nphys3757

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