A unified perspective on disorder in atomic systems: machine learning material properties and design

With its ability to leverage rapidly increasing amounts of data and computational resources, machine learning (ML) has the potential to be an indispensable tool in condensed matter physics and materials science. Our recent work is an application of ML to the study of the physics of metastable phases and their dynamics. We start by addressing the perennial question of whether disordered solids have localized defects. Using ML, we develop a structural model that is significantly more predictive than previous attempts, while still being interpretable and generalizable. Next, we embed the resulting ML representation in theoretical models of several phenomena in disordered solids, e.g. fragility, fragile-to-strong transition, out-of-equilibrium dynamics, aging, glassy thin film dynamics, mechanical response, and grain boundaries in polycrystals. This approach leads to a unified perspective on disordered particle arrangements, from atoms to macroscopic grains spanning seven orders of magnitude in particle size. In addition, I will present the application of similar approaches to the design of new materials and phases with desired properties. Finally, I will discuss concerns with the application of ML to atomic systems, particularly with regards to accuracy, safety and generalization.