In recent years, carbon emissions have been on the rise, with 35.8 Gt emitted globally in 2023. These escalating levels of greenhouse gases in the atmosphere demand immediate attention as they contribute significantly to climate change. To mitigate these effects, adsorption strategies for capturing and storing greenhouse gases are being studied. Pressure or temperature swing adsorption using porous adsorbents capable of capturing and releasing gases has been proposed as a solution. Metal-organic frameworks (MOFs) are an exemplary class of porous materials that have demonstrated promise not just for gas storage but also for separation and catalysis, due to their unique porous structures and tunable properties. With their high surface areas and customizable pore sizes, MOFs offer excellent potential for adsorbing and storing greenhouse gases such as methane (CH4) and carbon dioxide (CO2). The reticular nature of these MOF materials, consisting of inorganic secondary building units (SBUs) and organic linkers, leads to a large combinatorial space. However, a major limitation hindering the widespread application of MOFs is their poor stability, particularly under working conditions for gas adsorption applications, such as high pressure and temperature. Addressing this stability issue is crucial for ensuring the practical viability of MOFs in greenhouse gas capture and other environmental remediation efforts. The large space of candidate MOFs makes experimental identification of highly stable MOFs with target properties challenging and time-consuming. Therefore, computational screening methods have gained prominence as efficient tools for discovering novel MOFs. Several databases (DBs) of MOFs exist for computational screening, providing a diverse pool of MOF structures for exploration. Experimental MOFs have been curated by sanitizing single crystal structures of MOFs available from the Cambridge Structural Database (CSD) , whereas hypothetical MOFs are constructed by exhaustively enumerating combinations of building blocks. Some hypothetical DBs include the over 130,000 structures in the pioneering hMOF set, 300,000 structures in BW-DB, ~13,000 diverse structures in ToBaCCo, and around 280,000 structures in ARC-MOF, which aims to unify these disparate sets . Researchers have also curated and screened experimentally characterized MOFs in the roughly 10,000 MOFs identified in the computation-ready experimental MOF 2019 (CoRE MOF 2019) DB . In comparison to experimental MOFs, hypothetical MOFs tend to lack diversity as well as stability. The ultrastable MOF database (USMOF DB) is a hypothetical database curated by Nandy et al., which fills this stability gap by virtue of its construction process. The building blocks from the experimental CoRE MOF 2019 DB were extracted and recombined to make a new hypothetical dataset of 54,139 MOFs that comprised of an order of magnitude more topologies and SBUs than any prior hypothetical set. Among these MOFs, 9,524 MOFs were predicted to be "ultrastable" (i.e., activation stable and >1 standard deviation above the average thermal degradation temperature of 359 ℃) according to previously trained ML models for thermal and activation stability. The USMOF DB exhibits a 10-fold increase in the number of "ultrastable" MOF structures compared to existing hypothetical databases, while retaining metal diversity compared to the experimental CoRE MOF 2019 DB, making the USMOF DB a valuable resource for computational screening. In addition to thermal and activation stability, mechanical stability is also crucial for MOFs, as hydrostatic pressure can lead to amorphization, affecting their porosity and performance. Despite its importance, mechanical stability is one of several crucial measures rarely considered in computational screening efforts.