MOF Structure

Metal-organic frameworks (MOFs) are crystalline materials with highly tunable structures and properties, making them attractive for diverse applications like gas storage and carbon capture. Current research focuses on accelerating MOF discovery and design using machine learning techniques, including graph neural networks, transformer models, and diffusion models, often coupled with large datasets of experimental and computational data. These efforts leverage AI to predict MOF properties, optimize synthesis conditions, and even generate novel MOF structures, ultimately aiming to improve the efficiency and effectiveness of materials discovery for various technological needs. The development of comprehensive MOF databases and knowledge graphs further enhances the accessibility and utility of this research for the broader scientific community.