Neural-network-based order parameters for classification of binary hard-sphere crystal structures

Identifying crystalline structures is a common challenge in many types of research. Here, we focus on binary mixtures of hard spheres of various size ratios, which stabilise a range of crystal structures with varying complexity. We train feed-forward neural networks to distinguish different crystalline and fluid environments on a single-particle basis, by analysing vectors composed of several averaged local bond order parameters. For all size ratios considered, we achieve a classification accuracy above for all phases, meaning that our method is completely general and able to capture structural differences of a wide range of binary crystals.