Crystal Property

Predicting crystal properties is crucial for materials discovery and design, driving research into efficient and accurate computational methods. Current efforts focus on developing and applying machine learning models, particularly graph neural networks (GNNs) and transformers, often incorporating self-supervised learning techniques to address data scarcity and leveraging crystal symmetries for improved accuracy and efficiency. These advancements enable faster and more reliable prediction of various properties, including electronic structure, mechanical properties, and thermodynamic stability, accelerating materials research and development.