NUMERICAL PHYSICS

Machine learning glasses

Artificial neural networks now allow the dynamics of supercooled liquids to be predicted from their structure alone in an unprecedented way, thus providing a powerful new tool to study the physics of the glass transition.

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References

  1. 1.

    Bapst, V. et al. Nat. Phys. https://doi.org/10.1038/s41567-020-0842-8 (2020).

  2. 2.

    Widmer-Cooper, A., Harrowell, P. & Fynewever, H. Phys. Rev. Lett. 93, 135701 (2004).

    ADS  Article  Google Scholar 

  3. 3.

    Sussman, D. M., Schoenholz, S. S., Cubuk, E. D. & Liu, A. J. Proc. Natl Acad. Sci. USA 114, 10601–10605 (2017).

    ADS  MathSciNet  Article  Google Scholar 

  4. 4.

    Ninarello, A., Berthier, L. & Coslovich, D. Phys. Rev. X 7, 021039 (2017).

    Google Scholar 

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Correspondence to Giulio Biroli.

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Biroli, G. Machine learning glasses. Nat. Phys. 16, 373–374 (2020). https://doi.org/10.1038/s41567-020-0873-1

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