Amazing stuff! This kind of AI research opens up whole new worlds of discovery and understanding! This could be a breakthrough! What is next, protein folding, superconducting transitions, cell migration during embryonic development ...
For interested readers: You can gain access to the full Nature Physics article (behind a paywall) by using the link provided at the bottom of the corresponding Deepmind blog post.
"Despite decades of theoretical studies, the nature of the glass transition remains elusive and debated, while the existence of structural predictors of its dynamics is a major open question. Here we determine the long-time evolution of a glassy system solely from the initial particle positions and without any handcrafted features, using graph neural networks as a powerful model. We show that this method outperforms current state-of-the-art methods, generalizing over a wide range of temperatures, pressures and densities."
"These systems all operate under local constraints where the position of some elements inhibits the motion of others (termed frustration). Their dynamics are complex and cooperative, taking the form of large-scale, collective rearrangements which propagate through space in a heterogeneous manner."
DeepMind's AI models transition of glass from a liquid to a solid | VentureBeat: The DeepMind team trained a graph neural network that outperformed physics-inspired baselines and state-of-the-art AI models in predicting glassy dynamics.
AI system that predicts movement of glass molecules transitioning between liquid and solid states
DeepMind's blog post:
Towards understanding glasses with graph neural networks | DeepMind: Under a microscope, a pane of window glass doesn’t 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.
The underlying research paper:
Unveiling the predictive power of static structure in glassy systems | Nature Physics: Despite decades of theoretical studies, the nature of the glass transition remains elusive and debated, while the existence of structural predictors of its dynamics is a major open question. Recent approaches propose inferring predictors from a variety of human-defined features using machine learning. Here we determine the long-time evolution of a glassy system solely from the initial particle positions and without any handcrafted features, using graph neural networks as a powerful model. We show that this method outperforms current state-of-the-art methods, generalizing over a wide range of temperatures, pressures and densities. In shear experiments, it predicts the locations of rearranging particles. The structural predictors learned by our network exhibit a correlation length that increases with larger timescales to reach the size of our system. Beyond glasses, our method could apply to many other physical systems that map to a graph of local interaction. The physics that underlies the glass transition is both subtle and non-trivial. A machine learning approach based on graph networks is now shown to accurately predict the dynamics of glasses over a wide range of temperatures, p
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