DMREF: Collaborative: Computationally Driven Discovery and Engineering of Multiblock Polymer Nanostructures Using Genetic Algorithms

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****Technical Abstract****This computationally driven discovery program aims to disrupt the status quo for the design of multiblock materials. The research centers on the marriage of pseudo-spectral self-consistent field theory (SCFT) and real-space genetic algorithms (GAs), with a tight coupling to experimental synthesis and characterization. While widely used in polymer science and bioinformatics, respectively, SCFT and GAs have not been previously integrated to tackle problems of block polymer discovery and design. The approach adopted here addresses the challenges of large parameter spaces and polymorphism. It also solves the "inverse problem" of identifying multiblock sequences and compositions that can produce a desired nanoscale morphology, without resorting to exhaustive, unguided searches through parameter space. The computational effort is synergistically and iteratively combined with an experimental program that includes state-of-the-art synthesis, processing, and characterization tools. The experimental work will validate the computational methodology, including parameterization of the models, and provide inspiration for attractive and synthetically accessible design targets. This combined approach will dramatically reduce the timescale for discovery, design, and deployment of new multiblock polymers as advanced functional materials.****Non-Technical Abstract****This collaborative effort between researchers at the University of California, Santa Barbara and the University of Minnesota will develop discovery tools that will enable the rational, computationally-assisted design of multiblock polymers for applications in medicine, microelectronics, separations, and energy production and storage, among others. Complicating factors in this class of soft materials are the myriad parameters that dictate molecular architecture, block sequence, and interactions and the wide range of self-assembled nanostructures that are possible. Through a concerted and iterative combination of theory, simulation, and experiment, global optimization tools will be devised and validated to predict the forward and reverse relationship between polymer architecture and nanostructure. The discovery tools developed in this program will be made widely available to the industrial and academic polymer materials community through a web-based job submission program hosted at the Minnesota Supercomputer Institute, and a searchable database will be constructed from the structure/sequence/morphology maps that result over the course of the project. Outreach to industry will be accomplished by leveraging the established and highly successful industrial consortiums at UCSB (Complex Fluids Design Consortium) and UMN (IPrime). Personnel on the project will be trained in and enhance the rich multidisciplinary research environments afforded by the existing MRSECs at UMN and UCSB.This award is funded by the Division of Materials Research (DMR) and the Division of Mathematical Sciences (DMS).