Collaborative Research: Designing Functional Materials with Optimal Learning

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New products and material processing methods often require the identification of novel materials that are stronger, lighter, cheaper, or better in some way. Searching for new materials with a trial-and-error approach can be expensive and often ineffective. With this award, new mathematical methods and computer software will be developed to accelerate materials discovery. The planned approach will narrow the available options to those that are most likely to succeed, making discovery of new materials and processes more reliable and less costly. Demonstration of the approach will be made for materials to be used in flexible organic solar cells, but the methods could also be amenable to materials for use in pharmaceuticals or to food additives.A new optimal learning approach to materials design is planned that uses advances in Bayesian experimental design and machine learning to predict material properties from previous data and domain expertise, and to intelligently suggest physical and computational experiments that will provide information that is most supportive of discovery. These new mathematical techniques promise to greatly accelerate materials design, providing better materials more reliably and with less experimental effort. The approach will be demonstrated in the search for organic semiconductor materials over a set of existing candidates, solvent choices, and processing conditions, and integrate both physical and computational experiments in this search. The test case is an all-organic solar cell system of contorted hexabenzocoronenes (c-HBC), deposited on carbon nanotubes (CNT). This complex system involves issues including complexation between c-HBC and CNT at different processing conditions, etc., which provide a stringent test of optimal learning and computer simulation methods to predict the processing-structure-function triad. This approach is broadly applicable to a diverse set of materials design problems.