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

Project Personnel

Glenn Fredrickson

Principal Investigator

University of California, Santa Barbara

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Kris Delaney

University of California, Santa Barbara

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Marc Hillmyer

University of Minnesota, Twin Cities

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Kevin Dorfman

University of Minnesota, Twin Cities

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Frank Bates

University of Minnesota, Twin Cities

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Funding Divisions

Division of Materials Research (DMR), Division of Mathematical Sciences (DMS)

This collaborative effort between researchers at the University of California, Santa Barbara and the University of Minnesota developed discovery tools that 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 were devised and validated to predict the forward and reverse relationship between polymer architecture and nanostructure. Software tools emerging from the project have been hosted open source at the University of Minnesota. Outreach to industry was accomplished by leveraging the established and highly successful industrial consortiums at UCSB (Complex Fluids Design Consortium) and UMN (IPrime). Personnel on the project were trained in the rich multidisciplinary research environments afforded by the existing MRSECs at UMN and UCSB.