ML has seen success in small-molecule organic synthesis and is being introduced for synthetic polymers to accelerate discovery of new materials with desired structural or functional properties. In particular, ML can be exploited to classify large datasets, uncover hidden patterns, and reduce the number of variables for a multivariate dataset. By accomplishing these tasks, a data-driven approach can make predictions about material features of interest and further our understanding of how these features can be designed in future experiments. In this study, we combined high-throughput polymer synthesis and characterization with ML to aid the design of novel SCNPs that are compact and exhibit similar flexibility to ordered proteins.