A structural approach to relaxation in glassy liquids

By Sam Schoenholz, Ekin Dogus Cubuk, D. Sussman, Efthimios Kaxiras, A. J. Liu. 2015

431 citations recorded by the source corpus.

The relation between structure and dynamics in glasses is not fully understood. A new approach based on machine learning now reveals a correlation between softness—a structural property—and glassy dynamics. In contrast with crystallization, there is no noticeable structural change at the glass transition. Characteristic features of glassy dynamics that appear below an onset temperature, T0 (refs 1,2,3), are qualitatively captured by mean field theory4,5,6, which assumes uniform local structure. Studies of more realistic systems have found only weak correlations between structure and dynamics7,8,9,10,11. This raises the question: is structure important to glassy dynamics in three dimensions? We answer this question affirmatively, using machine learning to identify a new field, ‘softness’ which characterizes local structure and is strongly correlated with dynamics. We find that the onset of glassy dynamics at T0 corresponds to the onset of correlations between softness (that is, structure) and dynamics. Moreover, we construct a simple model of relaxation that agrees well with our simulation results, showing that a theory of the evolution of softness in time would constitute a theory of glassy dynamics.

Subfields: Biomedical Engineering, Condensed Matter Physics, Materials Chemistry, AI for Physical Sciences

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