Zhang, XiaoqiJablonka, Kevin MaikSmit, Berend2024-07-032024-07-032024-07-032024-06-1010.1039/d4dd00116hhttps://infoscience.epfl.ch/handle/20.500.14299/209095WOS:001248940500001This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs into a high-dimensional chemical space and suggests a pool of promising materials for specific applications based on user-endorsed MOFs with similarity analysis. This proposed approach significantly reduces the need for exhaustive labeling of every material in the database, focusing instead on a select fraction for in-depth investigation. Ranging from methane storage and carbon capture to quantum properties, this study illustrates the system's adaptability to various applications.|This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms.Physical SciencesTechnologyCarbon-DioxidePore-SizeFunctionalityTopologyDesigntext::journal::journal article::research article