Material Characterization

Material characterization focuses on determining the physical and chemical properties of materials, aiming to link these properties to their underlying structure and composition. Current research emphasizes the development of automated and high-throughput analysis techniques, leveraging machine learning models like diffusion models and generative adversarial networks, along with advanced algorithms such as Non-Negative Matrix Factorization and genetic algorithms, to analyze complex datasets from various spectroscopic and microscopic methods. This accelerates materials discovery and design, impacting diverse fields from waste management (through hyperspectral imaging analysis) to the preservation of historical artifacts (via image-based material classification), ultimately improving efficiency and enabling new scientific insights.