Why Is Glass Rigid? Signs of Its Secret Structure Emerge.


International Conference on Condensed Matter Physics





Most materials derive their macroscopic properties from their microscopic structure. A steel rod is hard, for instance, because its atoms form a repeating crystalline pattern that remains static over time. Water parts around your foot when you dip it into a lake because fluids don’t have that structure; their molecules move around randomly.

Then there’s glass, a strange in-between substance that has puzzled physicists for decades. Take a snapshot of the molecules in glass, and they’ll appear disordered just like a liquid’s. But most of the molecules barely move, making the material rigid like a solid.

Glass is formed by cooling certain liquids. But why the molecules in the liquid slow down so dramatically at a certain temperature, with no obvious corresponding change in their structural arrangement — a phenomenon known as the glass transition — is a major open question.


Now, researchers at DeepMind, a Google-owned artificial intelligence company, have used AI to study what’s happening to the molecules in glass as it hardens. DeepMind’s artificial neural network was able to predict how the molecules move over extremely long timescales, using only a “snapshot” of their physical arrangement at one moment in time. According to DeepMind’s Victor Bapst, even though the microscopic structure of a glass appears featureless, “the structure is maybe more predictive of the dynamics than people thought.”

Peter Harrowell, who studies the glass transition at the University of Sydney, agrees. He said the new work “makes a stronger case” than ever before that in glass, “structure somehow encodes for the dynamics,” and so glass isn’t as disordered as a liquid after all.

Predicting Propensity

To understand what microscopic changes cause the glass transition, physicists need to relate two kinds of data: how the molecules in a glass are arranged in space, and how they (slowly) move over time. One way to link these is with a quantity called dynamic propensity: how much a set of molecules is likely to have moved by some specific time in the future, given their current positions. This evolving quantity comes from calculating the molecules’ trajectories using Newton’s laws, starting with many different random initial velocities and then averaging the outcomes together.

By simulating these molecular dynamics, computers can generate “propensity maps” for thousands of glass molecules — but only on timescales of trillionths of a second. And molecules in glass, by definition, move extremely slowly. Computing their propensity to a horizon of seconds or more is “just impossible [for] normal computers because it takes too much time,” said Giulio Biroli, a condensed matter physicist at the École Normale Supérieure in France.

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