Metal Organic Framework

Metal-organic frameworks (MOFs) are crystalline materials with highly tunable structures and properties, making them attractive for diverse applications like gas storage and separation, catalysis, and energy storage. Current research heavily utilizes machine learning, employing models such as graph neural networks, transformers, and diffusion models, to predict MOF properties, design new structures, and accelerate the discovery process by analyzing large datasets and extracting information from scientific literature. This focus on data-driven approaches, coupled with advancements in AI-powered text mining and knowledge graph construction, promises to significantly enhance the efficiency and effectiveness of MOF research and development, leading to the creation of novel materials with tailored functionalities.