Autonomous Discovery of Functional Random Heteropolymer Blends through Evolutionary Formulation Optimization

While developing new polymers typically requires years of investigation, blending existing polymers offers a cost-effective strategy to create new materials. However, developing functional polymer blends is often a slow and challenging process due to their vast design space, the non-additive nature of polymer properties, and limited fundamental understanding to guide the optimization. Here, we report an autonomous platform that addresses these challenges by integrating high-throughput blending, real-time data acquisition, and an evolutionary algorithm for composition optimization. This approach enables rapid exploration of complex combinatorial blending spaces of random heteropolymers (RHPs). With enzyme thermal stability as a model objective, this system discovered random heteropolymer blends (RHPBs) that outperform all constituents. Retrospective analysis reveals segment-level interactions correlated with the performance. This work highlights the opportunity for materials discovery within the RHP and RHPB space and the immense potential of leveraging autonomous discovery platforms to accelerate the discovery of polymers with emergent properties.