Block polymers are macromolecules that contain segments or 'blocks' of repeated polymerized monomers of at least two types. Much as proteins have tremendous variation in property and function in biological systems by virtue of the choice and placement of amino acid residues along the polymer backbone, the properties of block polymers can be widely tuned by varying the length, placement, and chemical identity of their constituent blocks. Block polymers are the basis for many important types of soft materials such as elastomers and adhesives, but are increasingly important in applications such as advanced membranes for batteries and fuel cells, medical devices, and soft templates for patterning microelectronic devices. A current challenge in deploying block polymers in such applications is that the chemical design space is vast and there is very limited data and predictive ability connecting the chemical structure to the derivative properties in a given material. This project aims to dramatically accelerate block polymer materials discovery by closely coupling modern theory and simulation approaches with state-of-the-art synthesis and characterization. Through extensive experimental feedback to validate and continuously improve models and simulation methods, the project will build the foundations for a future in which in silico design of block polymers is routine.
Block polymers are attractive for creating advanced materials with novel functionality by embedding multiple physical or chemical properties within a single compound. Such polymers are also attractive for manufacturing as their synthesis is scalable and they embed nanostructures spontaneously by thermodynamic 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. Instead, the project adopts a computationally-driven materials discovery approach, building on recent game-changing advances in self-consistent field theory and global optimization strategies 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. Through experimental feedback to validate and continuously improve models and simulation methods, the project will build the foundations for a future in which in silico design of block polymers is routine.
