A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models
This work addresses material stability challenges for researchers in chemistry and materials science, offering a novel database but is incremental in building on existing screening methods.
The researchers tackled the problem of unknown stability in metal-organic frameworks (MOFs) by using machine learning to identify stable fragments and reassemble them into a new database of over 50,000 structures, resulting in an order of magnitude enrichment of ultrastable MOFs with improved thermal and mechanical stability.
High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases. This database shows an order of magnitude enrichment of ultrastable MOF structures that are stable upon activation and more than one standard deviation more thermally stable than the average experimentally characterized MOF. For the nearly 10,000 ultrastable MOFs, we compute bulk elastic moduli to confirm these materials have good mechanical stability, and we report methane deliverable capacities. Our work identifies privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.