Data-Driven Innovation in Metal-Organic Frameworks Photocatalysis: Bridging Gaps for CO2 Capture and Conversion with FAIR Principles

Abstract

Metal-organic frameworks (MOFs) have emerged as key materials for carbon capture and conversion, particularly in photocatalytic CO₂ 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 CO₂ 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 CO₂ capture and conversion.