Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO$_2$ Capture and Conversion with FAIR Principles

Metal-organic frameworks (MOFs) have emerged as key materials for carbon capture and conversion, particularly in photocatalytic CO$_2$ reduction. However, inconsistent reporting of essential parameters in the literature hinders informed decisions about material selection and optimization. This perspective highlights the need for a user-friendly, centralized database supported by automated data extraction using natural language processing tools to streamline comparisons of MOF materials. By consolidating crucial data from scientific literature, such a database promotes efficient decision-making in material selection for CO$_2$ capture and utilization. Emphasizing the significance of open-source initiatives and the principles of FAIR data—ensuring data are Findable, Accessible, Interoperable, and Reusable—a collaborative approach to data management and sharing is advocated for. Making the database-accessible worldwide enhances data quality and reliability, fostering innovation and progress in CO2 capture and conversion using MOF materials. Additionally, such databases are valuable in creating artificial intelligence tools to assist researchers in the discovery and synthesis of MOF materials for CO$_2$ capture and conversion.