Local classification of crystalline structures in complex plasmas using a PointNet

Abstract In complex plasmas, microparticles can form ordered crystalline structures under specific conditions. Accurately identifying these structures, such as face-centered cubic, hexagonal close-packed, and body-centered cubic, is a common task in physics. Previous methods rely on detecting symmetries in the spatial arrangement of particles, often requiring extensive calculations. This study presents a novel approach by utilizing a PointNet-based deep learning algorithm, called WignerNet, to classify these structures directly from three-dimensional reconstructions of their Voronoi cells. The model was trained exclusively on artificial and labeled data, incorporating various noise levels, and subsequently tested on real experimental data. The results demonstrate that our method effectively classifies structures, reducing computational complexity and improving accuracy compared to conventional techniques. This advancement opens up new possibilities for real-time analysis of complex plasma systems in various research.