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Register a new userEshelby ensemble of highly viscous flow out of equilibrium
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Authors
U. Buchenau
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The recent description of the highly viscous flow in terms of irreversible structural Eshelby rearrangements is extended to calculate the heat capacity of a glass former at a constant cooling rate through the glass transition. The result is compared to measured data from the literature, showing that the explanation works both for polymers and other glass formers.
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Supplemental Material to ArXiv:1902.02746
The recent Eshelby description of the highly viscous flow leads to the prediction of a factor of two different viscosities in stationary and alternating flow, in agreement with experimental evidence. The Kohlrausch barrier density increase with increasing barrier height finds a physical justification in the Adam-Gibbs increase of the number of structural alternatives of the Eshelby region with its increasing size. The new Ansatz allows to determine the number of atoms or molecules in the rearranging Eshelby domains from a combination of dynamic shear relaxation and calorimetric data.
The recent theoretical treatment of irreversible jumps between inherent states with a constant density in shear space is extended to a full theory, attributing the shear relaxation to structural Eshelby rearrangements involving the creation and annihilation of soft modes. The scheme explains the Kohlrausch exponent close to 1/2 and the connection to the low temperature glass anomalies. A continuity relation between the irreversible and the reversible Kohlrausch relaxation time distribution is derived. The full spectrum can be used in many ways, not only to describe shear relaxation data, but also to relate shear relaxation data to dielectric and bulk relaxation spectra, and to predict aging from shear relaxation data, as demonstrated for a very recent aging experiment.
The recent description of the cooling through the glass transition in terms of irreversible structural Eshelby rearrangements with a single average fictive temperature is extended to a distribution of fictive temperatures around the average one. The extension has only little influence on the cooling scans, but turns out to be necessary to understand the heating back to equilibrium.
Neural network based machine learning is emerging as a powerful tool for obtaining phase diagrams when traditional regression schemes using local equilibrium order parameters are not available, as in many-body localized or topological phases. Nevertheless, instances of machine learning offering new insights have been rare up to now. Here we show that a single feed-forward neural network can decode the defining structures of two distinct MBL phases and a thermalizing phase, using entanglement spectra obtained from individual eigenstates. For this, we introduce a simplicial geometry based method for extracting multi-partite phase boundaries. We find that this method outperforms conventional metrics (like the entanglement entropy) for identifying MBL phase transitions, revealing a sharper phase boundary and shedding new insight into the topology of the phase diagram. Furthermore, the phase diagram we acquire from a single disorder configuration confirms that the machine-learning based approach we establish here can enable speedy exploration of large phase spaces that can assist with the discovery of new MBL phases. To our knowledge this work represents the first example of a machine learning approach revealing new information beyond conventional knowledge.
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