Particle Shape

Particle shape analysis is crucial across diverse scientific fields, aiming to understand how particle morphology influences material properties and behavior in systems ranging from nanomaterials to biological samples and granular materials. Current research focuses on developing advanced machine learning techniques, including graph neural networks and various deep learning architectures, to classify, predict, and analyze particle shapes from diverse data sources like X-ray diffraction images and tactile sensor readings. These advancements are improving the accuracy and efficiency of characterization methods, with significant implications for materials science, biomedical imaging, and robotics, enabling better control over material synthesis and improved understanding of complex systems.