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

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

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.