PFI-TT: An Artificial Intelligence Capability to Accelerate Low-Cost Commercial Polymer Design

Award ID: 1941029

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project includes the creation of an accelerated low-cost capability for polymer selection, design and discovery for many industries. The proposed project will advance the development of machine learning for the traditional, laborious and expensive trial-and-error approaches to materials development. The total R&D expenditure of the polymer manufacturing industry is about $10 billion per year, with anticipated savings of one estimated $100 million per year in the United States. In addition, the acceleration of product design workflows could dramatically shorten time-to-market for new products. Finally, the proposed project includes entrepreneurial mentoring. The proposed project will help create a data-driven machine learning based capability and service to achieve accelerated application-specific polymer design and development. Machine learning (ML) algorithms trained on an underlying database produce predictive models, which can (1) make instantaneous predictions of properties of a new yet-to-be-synthesized polymer, and (2) make recommendations of new and existing polymers that will meet design objectives. The proposed project will advance the development of a prototype Polymer Genome online tool. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Jesus Soriano Molla