MIT News April 4, 2023
High-throughput screening of hypothetical metal-organic framework databases can uncover new materials, but their stability in real-world applications is often unknown. Researchers at MIT leveraged community knowledge and machine learning models to identify MOFs that are thermally stable and stable upon activation. They separated the MOFs into their building blocks and recombined them to make a new hypothetical MOF database of over 50,000 structures with orders of magnitude more connectivity nets and inorganic building blocks than were present in prior databases. This database showed a 10-fold enrichment of ultrastable MOF structures that were stable upon activation and more than 1 standard deviation more thermally stable than the average experimentally characterized MOF. For nearly 10,000 ultrastable MOFs, they computed elastic moduli to confirm that these materials had good mechanical stability and reported methane deliverable capacities. They identified privileged metal nodes in ultrastable MOFs that optimized gas storage and mechanical stability simultaneously… read more. TECHNICAL ARTICLE
Graphical abstract. Credit: Matter, April 04, 2023