The deepest and most interesting unsolved problem in solid state theory is probably the theory of the nature of glass and the glass transition. Conclusion
Our results show that graph networks constitute a powerful tool to predict the long term dynamics of glassy systems, leveraging the structure hidden in a local neighborhood of particles. We expect our technique to be useful for predicting other physical quantities of interest in glasses, and hope that it will lead to more insights for glassy system theorists – we are open-sourcing our models and trained networks to aid this effort. More generally, graph networks are a versatile tool that are being applied to many other physical systems that consist of many-body interactions, in contexts including traffic, crowd simulations, and cosmology. The network analysis methods used here also yield a deeper understanding in other fields: graph networks may not only help us make better predictions for a range of systems, but indicate what physical correlates are important for modeling them – in this work, how interactions between local particles in a glassy material evolve over time.
We believe that our results advocate using structured models when applying machine learning to the physical sciences; in our case, the ability to analyse the inner workings of a neural network indicated that it had discovered a quantity that correlates with an elusive physical quantity. This demonstrates that machine learning can be used not only to make quantitative predictions, but also to gain qualitative understanding of physical systems. This could mean that machine learning systems might be able to eventually assist researchers in deriving fundamental physical theories, ultimately helping to augment, rather than replace, human understanding.