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Networks and interfaces as catalysts for polymer materials innovation

Published

Author(s)

Michael Deagen, Dylan Walsh, Debra Audus, Kenneth Kroenlein, Juan de Pablo, Kaoru Aou, Kyle Chard, Klavs Jensen, Bradley Olsen

Abstract

Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial intelligence (AI) and machine learning (ML)—play a foundational role in materials research and development. This perspective draws attention to the roles of networks and interfaces—of and between humans and machines—for the purpose of generating knowledge and accelerating innovation. Polymers, a class of materials with massive global impact, present a unique opportunity for the application of informatics and automation to pressing societal challenges. To develop these networks and interfaces in polymer science, the Community Resource for Innovation in Polymer Technology (CRIPT)—a polymer data ecosystem based on novel polymer data model, representation, search, and visualization technologies—is introduced. The ongoing co-design efforts engage stakeholders in industry, academia, and government to uncover rapidly actionable, high-impact opportunities to build networks, bridge interfaces, and catalyze innovation in polymer technology.
Citation
Cell Reports Physical Science

Keywords

polymers, databases, autonomous experiments

Citation

Deagen, M. , Walsh, D. , Audus, D. , Kroenlein, K. , de Pablo, J. , Aou, K. , Chard, K. , Jensen, K. and Olsen, B. (2022), Networks and interfaces as catalysts for polymer materials innovation, Cell Reports Physical Science, [online], https://doi.org/10.1016/j.xcrp.2022.101126, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935159 (Accessed March 1, 2024)
Created October 27, 2022, Updated January 30, 2023