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High-throughput screening of large hypothetical databases of metal-organicframeworks (MOFs) can uncover new materials, but their stability in real-worldapplications is often unknown. We leverage community knowledge and machinelearning (ML) models to identify MOFs that are thermally stable and stable uponactivation. We separate these MOFs into their building blocks and recombinethem to make a new hypothetical MOF database of over 50,000 structures thatsamples orders of magnitude more connectivity nets and inorganic buildingblocks than prior databases. This database shows an order of magnitudeenrichment of ultrastable MOF structures that are stable upon activation andmore than one standard deviation more thermally stable than the averageexperimentally characterized MOF. For the nearly 10,000 ultrastable MOFs, wecompute bulk elastic moduli to confirm these materials have good mechanicalstability, and we report methane deliverable capacities. Our work identifiesprivileged metal nodes in ultrastable MOFs that optimize gas storage andmechanical stability simultaneously.

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models