Designing polymers with targeted structural and functional properties is challenging due to the practically infinite polymer chemistry design space. Trial-and-error or intuition-based strategies are not efficient, and they are likely to miss optimal solutions due to the complexity of chemical composition and morphology of polymers. Furthermore, these strategies with traditional experimental and computational routes are time and resource consuming. Machine Learning (ML) models trained on polymer data sets can mitigate this problem, as it is possible to predict a new material's properties instantaneously by interpolating within an existing dataset . There have been a number of studies in the recent literature that leveraged ML to predict the properties of polymers. For example, Alves et al. developed models to discover polymeric micelle formulations for poorly soluble drugs using micellar solubilization data .