Molecular simulations can help navigate the search space, but the highest-fidelity simulations are too computationally intensive to investigate all of the options. An alternate, less computationally demanding approach is to use machine learning (ML). With ML algorithms, one can map the relationships between adsorbent molecular structure and separation performance and quickly generate full isotherms of whole libraries of both experimentally synthesized MOFs, such as the Computation Ready Experimental (CoRE) database, as well as hypothetical MOFs. Several studies utilize these databases in conjunction with ML to screen MOFs for adsorption applications. Tang et al. developed a random forest model using a dataset generated through Grand Canonical Monte Carlo (GCMC) simulations of propane and propylene on a subset of the CoRE database and successfully identified nine MOFs with both high propane selectivity and uptake capacity. Some studies have gone even further and used genetic algorithms to propose entirely new MOF structures based on ML modeling results. For example, Han and Kim used PORMAKE to generate hypothetical structures over six generations of a genetic algorithm, evaluating ethane/ethylene selectivity via ML-predicted Henry's coefficients. Another tactic that has been pursued is predicting whole isotherms of adsorbates under different conditions. Anderson et al. took the unique approach of predicting full isotherms on a range of small, low-interaction gases and generalizing their model to make predictions of any small, near-spherical adsorbate. These and other studies demonstrate that ML is a powerful tool for quickly identifying MOFs that are potentially useful for adsorption applications. However, there is not yet a tool for predicting full adsorption isotherms for C2 molecules, which limits MOF selection to only the process conditions intrinsic to the model training data instead of enabling the selection of the best material for a given process.