I-Corps: Using machine learning methods and polymer data to predict properties of new polymers and accelerate application-specific polymer design

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The broader impact/commercial potential of this I-Corps project is the development of a software tool for polymer selection, design and discovery. Potential customers of this technology include polymer chemists or materials designers in companies that manufacture or utilize polymers in industries such as auto, chemicals, and oil and gas. The value of this technology lies in overcoming 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. This technology has the potential to save an estimated savings of $100 million per year. In addition, the acceleration of product design workflows could dramatically shorten time-to-market for products that must undergo evolutionary changes throughout the product’s life-cycle.This I-Corps project is based on the development of a data-driven machine learning tool 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 goal is to create a customized application-specific polymer design tool for end-user companies, and a generic data-to-model product line that ingests in-house proprietary (legacy/historic) polymer data to produce predictive models for polymer manufacturing companies.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.