Crystal Structure Prediction

Crystal structure prediction (CSP) aims to computationally determine the most stable atomic arrangement of a material given its chemical composition, a crucial step in materials discovery. Current research heavily utilizes machine learning, employing diverse approaches such as deep learning models (e.g., diffusion models, graph neural networks), multi-objective optimization algorithms (e.g., Quality-Diversity methods), and surrogate models to accelerate the computationally expensive process of searching for optimal structures. These advancements enable efficient screening of vast chemical spaces, accelerating the discovery of novel materials with tailored properties for applications ranging from energy storage to electronics. The integration of experimental data, such as powder X-ray diffraction patterns, further enhances prediction accuracy and applicability.