While we train ML models using both chemical descriptors and MFFs as inputs, for simplicity, we only screen new polymers using MFFs as model inputs. The feature spaces for fingerprints across the datasets studied in this work are visualized using uniform manifold approximation and projection (UMAP) in Fig. of Supporting Information. In general, our training set, Dataset A, spans across the screening space of Datasets B, C, and D. Thus, our ML models can learn across a wide chemical feature space of interest. While Datasets B and C have more complete coverage due to the sheer number of samples, Dataset D only includes ladder polymers, which explains why they are more confined in the feature space.