Disconnecting structure and dynamics in glassy thin films
arXiv:1610.03401 · doi:10.1073/pnas.1703927114
Abstract
Nanometrically thin glassy films depart strikingly from the behavior of their bulk counterparts. We investigate whether the dynamical differences between bulk and thin film glasses can be understood by differences in local microscopic structure. We employ machine-learning methods that have previously identified strong correlations between local structure and particle rearrangement dynamics in bulk systems. We show that these methods completely fail to detect key aspects of thin-film glassy dynamics. Furthermore, we show that no combination of local structural features drawn from a very general set of two- and multi-point functions is able to distinguish between particles at the center of film and those in intermediate layers where the dynamics are strongly perturbed.
8 pages, 7 figures
References in corpus (10)
- Irreversible reorganization in a supercooled liquid originates from localised soft modes
- Identifying structural flow defects in disordered solids using machine learning methods
- On the surface of glasses
- The building blocks of dynamical heterogeneities in dense granular media
- The Relationship Between Local Structure and Relaxation in Out-of-Equilibrium Glassy Systems
- Structure and dynamics in glass-formers: predictability at large length scales
- Macroscopic Facilitation of Glassy Relaxation Kinetics: Ultra Stable Glass Films with Front-Like Thermal Response
- Cooperative Strings and Glassy Interfaces
- Length Scale of Correlated Dynamics in Ultra-thin Molecular Glasses
- Spatial distribution of entanglements in thin free-standing films
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