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SBIR Phase I: A Physics-Informed/Encoded Polymer Informatics Platform for Accelerated Development of Advanced Polymers and Formulations

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2322108
Agency Tracking Number: 2322108
Amount: $273,706.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AI
Solicitation Number: NSF 23-515
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-10-01
Award End Date (Contract End Date): 2024-09-30
Small Business Information
75 5th St
Atlanta, GA 30308
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Huan Tran
 (330) 559-0752
Business Contact
 Huan Tran
Phone: (330) 559-0752
Research Institution

The broader/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project are to transform the way in which polymeric materials are developed. Adopting the most advanced artificial intelligence (AI) techniques, the proposed technology seeks to dramatically accelerate the exploration of new polymer formulations, efficiently and accurately discovering those with targeted performances and applications, and ultimately minimizing the time and the cost needed to develop new and superior functional materials. This technology will enable the targeted development of polymers for specific applications such as packaging or energy storage, while ensuring full recyclability. New polymer designs of this type can help alleviate the current global problem of plastic waste. Given that polymers are one of the most important classes of materials in use today, the impact of this SBIR Phase I project is expected to be significant and far-reaching. _x000D_
This Small Business Innovation Research (SBIR) Phase I project aims at transforming the state-of-the-art AI-based technology currently used to discover and design functional polymers. Since the beginning of polymer informatics about a decade ago, this AI-based approach has quickly become a powerful tool to design new functional polymers. At the center of this technology are the machine-learning models, trained on past data and used to evaluate the polymeric materials yet to be synthesized. Currently, the models are developed by purely “learning” the available datasets independently, ignoring numerous physics-governed correlations across data of different polymer classes and properties that come from different sources. Without proper awareness, the models can easily violate the relevant physic rules and render unphysical results, especially when the training data are not sufficiently large. In this project, the company will develop two deep learning architectures in which known and important physics-governed correlations are secured. The architectures will be the most advanced deep learning tools to combat the small and sparse data problems that are very common in and important for polymer informatics. The new technology is expected to significantly transform the development and deployment of functional polymers._x000D_
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.

* Information listed above is at the time of submission. *

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