Accelerating Materials Discovery: Artificial Intelligence for Sustainable, High-Performance Polymers
Program Document
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OSTI ID:1875833
PolyID enables the discovery of polymers with advanced performance and greater sustainability while reducing material development timelines. The material design space is immense and cannot be reasonably probed using an Edisionian approach. High-throughput property prediction, enabled by artificial intelligence provides a hypothesis driven approach for down selection of candidate polymers to pursue experimentally. To aid experimentalists in the down selection of material targets this high-throughput, machine learning-based tool is capable of predicting polymer properties simply from molecular structures. Currently, transport, thermal, and mechanical properties across 7 polymer class (polyamides, polyesters, polycarbonates, polyimides, polyolefins, polyacrylates, and polyurethanes) can be predicted, and the PolyID platform has been flexibly designed so new materials and properties can be added.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1875833
- Report Number(s):
- NREL/FS-2800-83233; MainId:84006; UUID:03607a87-033a-46e3-a52d-cbd95f3c2a24; MainAdminID:64826
- Country of Publication:
- United States
- Language:
- English
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