Designing multi-responsive polymers using latent variable methods

The design of stimulus-responsive materials, particularly those intended to respond to more than one stimulus, is an inherently challenging and typically trial-and-error process involving multiple synthesis/characterization iterations in the laboratory. In this work, latent variable models are applied to existing, “failed” polymer formulations and characterizations to facilitate the rational design of materials with specific, targeted properties and to predict responsive polymer properties before synthesizing the materials in the laboratory. The models are capable of simultaneously predicting three targeted polymer properties (cloud point, molecular weight, and % recovery of polymer mass) for poly(N-isopropylacrylamide)-based materials that can be reversibly photo-crosslinked. Model inversion and optimization are used to identify new polymer formulations that exhibit significantly improved properties relative to the formulations developed by chemical intuition based on available literature. This model-based design approach moves away from the traditional trial-and-error approach to save time, energy, and resources in the production of novel materials while at the same time generating responsive polymers with improved properties.