  Anisotropy in crystals plays a pivotal role in many technological
applications. For example, anisotropic electronic and thermal transport are
thought to be beneficial for thermoelectric applications, while anisotropic
mechanical properties are of interest for emerging metamaterials, and
anisotropic dielectric materials have been suggested as a novel platform for
dark matter detection. Understanding and tailoring anisotropy in crystals is
therefore essential for the design of next-generation functional materials. To
date, however, most data-driven approaches have focused on the prediction of
scalar crystal properties, such as the spherically averaged dielectric tensor
or the bulk and shear elastic moduli. Here, we adopt the latest approaches in
equivariant graph neural networks to develop a model that can predict the full
dielectric tensor of crystals. Our model, trained on the Materials Project
dataset of c.a. 6,700 dielectric tensors, achieves state-of-the-art accuracy in
scalar dielectric prediction in addition to capturing the directional response.
We showcase the performance of the model by discovering crystals with almost
isotropic connectivity but highly anisotropic dielectric tensors, thereby
broadening our knowledge of the structure-property relationships in dielectric
crystals.
