Computational Discovery of Metal-organic Frameworks for Sustainable Energy Systems: Open Challenges

Abstract

Metal–Organic Frameworks (MOFs) are promising functional microporous materials for a variety of next-generation sustainable energy systems. Their large design space makes it impossible to synthesize, test, and screen them all to identify best candidates. The computational discovery of MOFs has thus become a popular research topic, with methodological advances in computational chemistry and data science heavily contributing to this. Structure databases, materials representation, property evaluation methodologies, performance metrics, and search algorithms all pose open challenges for the community to solve. These challenges are summarized and briefly discussed in this study, with a focus on the engineering aspects required for computational MOF discovery to become a reliable tool for industry. As computational discovery workflows are complicated and necessitate skills from a variety of disciplines, bridging the knowledge gap and enhancing collaboration are critical. Despite the challenges, we remain optimistic about the great potential of computational MOF discovery technology.