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Found 4 papers, 2 papers with code
Elastoplasticity Mediates Dynamical Heterogeneity Below the Mode-Coupling Temperature
As liquids approach the glass transition temperature, dynamical heterogeneity emerges as a crucial universal feature of their behavior.
Soft Condensed Matter Statistical Mechanics Chemical Physics
Tilting the playing field: Dynamical loss functions for machine learning
In underparameterized networks, such dynamical loss functions can lead to successful training for networks that fail to find a deep minima of the standard cross-entropy loss.
Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function
We compare glassy dynamics in two liquids that differ in the form of their interaction potentials.
Machine learning determination of atomic dynamics at grain boundaries
In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics.
Materials Science
