High-throughput screening of hypothetical metal-organic framework (MOF) databases 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 with orders of magnitude more (1) connectivity nets and (2) inorganic building blocks than were present in prior databases. This database shows a 10-fold enrichment of ultrastable MOF structures that are stable upon activation and more than 1 standard deviation more thermally stable than the average experimentally characterized MOF. For nearly 10,000 ultrastable MOFs, we compute elastic moduli to confirm that these materials have good mechanical stability, and we report methane deliverable capacities. We identify privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.

假设的金属有机框架 (MOF) 数据库的高通量筛选可以发现新材料，但它们在实际应用中的稳定性通常是未知的。我们利用社区知识和机器学习 (ML) 模型来识别热稳定和激活后稳定的 MOF。我们将这些 MOF 分成它们的构建块，然后将它们重新组合以创建一个新的假设 MOF 数据库，该数据库包含超过 50,000 个结构，其 (1) 连接网络和 (2) 无机构建块比之前的数据库中存在的数量级多。该数据库显示了超稳定 MOF 结构的 10 倍富集，这些结构在激活时稳定，并且比平均实验表征的 MOF 热稳定性高 1 个标准偏差以上。对于近 10,000 个超稳定 MOF，我们计算弹性模量以确认这些材料具有良好的机械稳定性，并且我们报告了甲烷输送能力。我们确定了超稳定 MOF 中的特权金属节点，可同时优化气体存储和机械稳定性。