Modeling Grain Boundaries using Scattering Transformations for use in Machine Learning

Grain boundaries of crystalline materials have a major impact upon their physical properties. Therefore the key to truly understanding materials is to understand grain boundaries. Only within the last year has the materials community successfully been able to model grain boundaries and predict material properties by adopting techniques from machine learning. Due to the sparseness of grain-boundary data, the representation (i.e., how the data is represented for the machine), of the grain boundary is critical to successfully modeling grain boundaries. I will discuss a novel grain boundary representation, the Scattering Transformation, and use it to understand grain boundary properties such as energy, mobility, and shear coupling.