While there are many ways to perform ensembling, we choose to use bootstrapping, or training each model in the ensemble with a different random subset of the training data. First, we randomly select 20% of the data to be the holdout set, which is used for performance scoring. 16 independent models are trained, using 80% of the entries in the non-holdout set each time, selected at random. Alongside the models trained in Step 3 of our workflow, we can perform explainable ML. To strengthen our physical understanding of how chemical features are linked to performance in gas separation membranes, our primary tool involves assessing SHAP values from each model . In essence, the SHAP approach considers how well a model performs when each feature is neglected during training. By analyzing the quantitative impact of leaving out a feature on the model prediction, a feature importance can be assigned. Moreover, each sample's impact on the final model prediction can also be evaluated.