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Can the glass transition be explained without a growing static length scale?

Ludovic BerthierGiulio BiroliJean-Philippe BouchaudGilles Tarjus
Published in: The Journal of chemical physics (2019)
It was recently discovered that SWAP, a Monte Carlo algorithm that involves the exchange of pairs of particles of differing diameters, can dramatically accelerate the equilibration of simulated supercooled liquids in regimes where the normal dynamics is glassy. This spectacular effect was subsequently interpreted as direct evidence against a static, cooperative explanation of the glass transition such as the one offered by the random first-order transition (RFOT) theory. We explain the speedup induced by SWAP within the framework of the RFOT theory. We suggest that the efficiency of SWAP stems from a postponed onset of glassy dynamics. We describe this effect in terms of "crumbling metastability" and use the example of nucleation to illustrate the possibility of circumventing free-energy barriers of thermodynamic origin by a change in the local dynamical rules.
Keyphrases
  • monte carlo
  • machine learning
  • deep learning
  • neural network