X. Zhang, K. M. Jablonka, and B. Smit, Deep learning-based recommendation system for metal–organic frameworks (MOFs), Digit Discov 3 (7), 1410 (2024) DOI: 10.1039/D4DD00116H
Abstract: This 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.