Carbon capture has emerged as a promising solution to mitigate carbon dioxide (CO2) emissions across diverse industrial processes, addressing the challenge of climate change. Within adsorptive technologies for carbon capture, Metal-Organic Frameworks (MOFs) have attracted considerable interest as selective CO2 adsorbents due to their modular, tunable properties. Over the years, more than 100,000 MOFs have been synthesized and reported in the literature , and trillions of materials have been hypothesized to exist using the Lego-like principles of MOF construction. This presents a challenge in identifying the most suitable materials for carbon capture applications. To address this, high-throughput computational modeling must be applied for large-scale screening of MOFs for specific applications. Starting with the pioneering work of Wilmer and co-workers more than a decade ago, notable early contributions include Haldoupis and co-workers , Lin et al. , Huck et al. , to name a few. These efforts, their evolution over the last decade and key challenges have been recently reviewed in Ref . The last five or so years of this field are associated with two important trends. The first trend reflects a shift in the computational screening strategies from using material level metrics, such as CO2/N2 selectivity or CO2 capacity, to evaluation of the materials at a process level, capturing complexities of the post-combustion Pressure Swing Adsorption (PSA) cycle. Recent examples of this approach include Farmahini et al. , Yancy-Caballero et al. , Cleeton et al. , Deng et al. , and more recently, Young et al. . Farmahini and co-workers reviewed the fundamentals of this multiscale strategy in Ref . The second trend is the growing role of machine-learning (ML) and generative methods.