Because we train our ML models across a range of pressure conditions, we compared the performance across different regimes to determine relative accuracy and ease of prediction. To make this comparison, we employed the same train/test group split as in full training but segregated the test data into five groupings based on the pressure of the data point and calculated mean average percent error (MAPE) for each grouping separately. We use MAPE instead of MAE here to normalize across expected uptake values. For both the ethane and ethylene models, the MAPE declines from the lowest-pressure regime, 0-20 kPa, to the highest-pressure regime, 80-100 kPa (Figure ). In the lower-pressure regimes, surface effects between the adsorbent and adsorbate that are determined by their respective chemistries will be more dominant. It appears that the ML models struggle to capture this relationship. On the other hand, MAPE is significantly reduced in the higher-pressure regimes as surface chemistry becomes less important and bulk geometric features start to dominate. Nevertheless, the 80-100 kPa range is the most relevant to real world performance because ethane/ethylene sorbent separations are usually performed at near atmospheric conditions. The relatively higher accuracy in this region of the isotherm suggests that the models are able to estimate ethane/ethylene separation performance under real-world operating conditions. With these models, we screened the CoRE MOF 2019 dataset for promising ethaneselective structures for which the model predictions have high confidence. We used the models to generate predicted isotherms on the MOFs in the CoRE MOF 2019 set at 298 K. We limited our search to the structures for which the models had higher confidence, as determined by latent space distance (see Sec. 2). By evaluating the relationship between the average latent space distance of each test set structure to its ten closest neighbors in the training set and the average test set error across all measurements of the structure, we determined distance cutoffs below which ML model predictions will have less uncertainty (Supporting Information Figure ). Starting from the 9,597 featurized CoRE structures, these cutoffs retain 4,428 CoRE structures with confident ethane isotherms, 4,154 with confident ethylene isotherms and 1,987 overlapping structures between the two sets. With these predictions, we screened for highly ethane-selective MOFs.