Crystal Structure

Crystal structure research focuses on understanding and predicting the arrangement of atoms in crystalline materials, aiming to correlate structure with material properties. Current research heavily utilizes machine learning, employing graph neural networks, diffusion models, and transformers to analyze and generate crystal structures, often incorporating techniques like equivariance to account for inherent symmetries. These advancements are significantly impacting materials science by accelerating the discovery of novel materials with desired properties and improving the efficiency of materials characterization techniques like X-ray diffraction analysis. The ultimate goal is to enable rational design of materials for specific applications.