Accelerating Materials Discovery: Artificial Intelligence for Sustainable, High-Performance Polymers

Nolan Wilson, Mark Nimlos

Research output: NRELFact Sheet


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.
Original languageAmerican English
PublisherNational Renewable Energy Laboratory (NREL)
StatePublished - 2022

NREL Publication Number

  • NREL/FS-2800-83233


  • artificial intelligence
  • machine learning
  • performance advantaged bioproducts
  • polymers


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