Towards understanding glasses with graph neural networks
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.Read More
Related Google News:
- Scaling deep retrieval with TensorFlow Recommenders and Vertex AI Matching Engine May 1, 2023
- Scalable electronic trading on Google Cloud: A business case with BidFX May 1, 2023
- Effingo: the internal Google copy service moving data at scale May 1, 2023
- Google at ICLR 2023 May 1, 2023
- Serving With TF and GKE: Stable Diffusion April 28, 2023
- Cloud CISO Perspectives: Late April 2023 April 28, 2023
- How Bud Financial turns transactional data into rich customer insight April 28, 2023
- An ML-based approach to better characterize lung diseases April 27, 2023