High Entropy Alloy

High-entropy alloys (HEAs), composed of multiple principal elements, are attracting significant research interest due to their often superior and tunable properties. Current research focuses on developing efficient computational methods, including graph neural networks and variational autoencoders, to predict HEA properties and accelerate materials discovery, often incorporating supply chain risk assessment into the design process. These efforts leverage machine learning algorithms and large language models to analyze existing data, generate new design hypotheses, and optimize alloy compositions for specific applications, bridging the gap between semantic and numerical material descriptions. This improved design process promises to significantly enhance the development of high-performance, sustainable materials for various industries.