Alloy Property

Alloy property research focuses on predicting and optimizing material characteristics based on composition and processing. Current efforts leverage machine learning, employing diverse architectures like crystal graph neural networks, variational autoencoders, and large language models (LLMs) to establish relationships between alloy microstructure, composition, processing parameters, and resulting properties. This work is crucial for accelerating materials discovery and design, enabling the development of alloys with tailored properties for applications ranging from energy storage to advanced manufacturing. The integration of physics-based simulations and multi-agent AI systems further enhances the efficiency and accuracy of alloy design.