The practical implications of modelling glass
The glass transition is a ubiquitous phenomenon which manifests in more than window (silica) glasses. For instance, when ironing, polymers in a fabric are heated, become mobile, and then oriented by the weight of the iron. More broadly, a similar and related transition, the jamming transition, can be found in colloidal suspensions (such as ice cream), granular materials (such as a static pile of sand), and also biological systems (e.g., for modelling cell migration during embryonic development) as well as social behaviours (for instance traffic jams). 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. Glasses are considered to be archetypal of these kinds of complex systems, and so better understanding them will have implications across many research areas. This understanding might yield practical benefits – for example, creating materials that have a more stable glass structure, instead of a crystalline one, would allow them to dissolve quickly, which could lead to new drug delivery methods. Understanding the glass transition may result in other applications of disordered materials, in fields as diverse as biorenewable polymers and food processing. The study of glasses has also already led to insights in apparently very different domains such as constraint satisfaction problems in computer science and, more recently, the training dynamics of under-parameterized neural networks.
A deeper understanding of glasses may lead to practical advances in the future, but their mysterious properties also raise many fundamental research questions. Though humans have been making silica glasses for at least four thousand years, they remain enigmatic to scientists: there are many unknowns about the underlying physical correlates of, for example, the trillion-fold increase in viscosity that happens over the cooling process. Our interest in this field was also motivated by the fact that glasses are also an excellent testbed for applying modern machine learning methods to physical problems: they’re easy to simulate, and easy to input to particle-based machine learning models. Crucially, we can then go in and examine these models to understand what they’ve learned about the system, to gain deeper qualitative insights about the nature of glass, and the structural quantities which underpin its mysterious dynamical qualities. Our new work, published in Nature Physics, could help us gain an understanding of the structural changes that may occur near the glass transition. More practically, this research could lead to insights about the mechanical constraints of glasses (e.g., where a glass will break).
Leveraging graph neural networks to model glassy dynamics
Glasses can be modelled as particles interacting via a short-range repulsive potential which essentially prevents particles from getting too close to each other. This potential is relational (only pairs of particles interact) and local (only nearby particles interact with each other), which suggests that a model that respects this local and relational structure should be effective. In other words, given the system is underpinned by a graph-like structure, we reasoned it would be best modeled by a graph structured network, and set out to apply Graph Neural Networks to predict physical aspects of a glass.
We first created an input graph where the nodes represent particles, and edges represent interactions between particles, and are labelled with their relative distance. A particle was connected to its neighboring particles within a certain radius (in this case, 2 particle diameters). We then trained a neural network, described below, to predict a single real number for each node of the graph. This prediction was ultimately regressed towards the mobilities of particles obtained from computer simulations of glasses. Mobility is a measure of how much a particle typically moves (more technically, this corresponds to the average distance travelled when averaging over initial velocities).
Source: https://deepmind.com/blog/article/Towards-understanding-glasses-with-graph-neural-networks
