To survey the molecular space of potential carbonate for steric and electronic features which may correlate with lower depolymerization barrier height, we first selected seven core carbonate monomers (1a-g, Fig. ) which exhibit systematically varied steric and electronic effects while still being computationally approachable for DFT optimizations. To expand this dataset for exploration using the more efficient DFTB methodology, we first mined 196 cyclic carbonate monomers with pendant chains contain 30 atoms or less from patent literature using IBM's Chemical Information Resources for Cognitive Analytics (CIRCA) platform as well as from historical experimental data. We next computed both the depolymerization barrier height and 1613 Mordred features for corresponding linear carbonates of 1a-g (Fig. ). Using the top 20 most positively and negatively correlated features, we calculated the distribution of the features for all CIRCA-found molecules and reduced the feature set by removing features with similar distributions as determined with a Kolmogorov-Smirnov test and a p-value < 0.05. For each of the remaining 27 Mordred features, we divided the range of possible feature values into 10 bins and select one molecule to represent each bin range, provided it is not already represented by 1a-g (Fig. ). Following this procedure, we identified 26 additional molecules which were used in the DFTB barrier protocol to examine property-barrier trends.