Computationally-Driven Design of Advanced Block Polymer Nanomaterials
Block polymers are attractive for creating advanced materials with novel functionality by embedding
multiple physical or chemical properties within a single nanostructured compound. Such polymers are
also attractive for manufacturing as their synthesis is scalable and the nanostructures spontaneously form
by driving forces arising from the incompatibility of the different blocks. However, as the demand for
distinct desirable properties exhibited by a single material increases, so must the number of blocks. The
corresponding design space increases geometrically with the number of blocks and block chemistries,
making an intuition-based, trial-and-error approach infeasible. Our project involves a computationally-driven materials discovery approach, building on recent game-changing advances in self-consistent field theory (SCFT) for materials design and discovery. These computational strategies are coupled to an ambitious, advanced synthesis and characterization program capable of realizing the desired materials in practice, with extensive feedback between experiment and computation.