Towards understanding glasses with graph neural networks
fetched at April 6, 2020

Under a microscope, a pane of window glass doesnt look like a collection of orderly molecules, as a crystal would, but rather a jumble with no discernable structure. Glass is made by starting with a glowing mixture of high-temperature melted sand and minerals. Once cooled, its viscosity (a measure of the friction in the fluid) increases a trillion-fold, and it becomes a solid, resisting tension from stretching or pulling. Yet the molecules in the glass remain in a seemingly disordered state, much like the original molten liquid almost as though the disordered liquid state had been flash-frozen in place. The glass transition, then, first appears to be a dramatic arrest in the movement of the glass molecules. Whether this process corresponds to a structural phase transition (as in water freezing, or the superconducting transition) is a major open question in the field. Understanding the nature of the dynamics of glass is fundamental to understanding how the atomic-scale properties define the visible features of many solid materials.

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