National Science Foundation Invests $2M in AI Investigation to Advance Sustainable Biopolymers

The National Science Foundation has invested $2 million in a new artificial intelligence investigation to advance sustainable biopolymer production, addressing a global challenge in climate change and environmental health.

The AI investigation will focus on developing an integrated machine learning and robotics design system to advance biopolymers, a biodegradable alternative that could reduce carbon emissions and the reliance on fossil fuels. The research will be led by Assistant Professor Po-Yen Chen in the Department of Chemical and Biomolecular Engineering in collaboration with Teng Li, a professor in the Department of Mechanical Engineering and Sanghamitra Dutta, an assistant professor in the Department of Electrical and Computer Engineering as well as other researchers in Iowa State University.

The research will address the global footprint of plastics production, which was attributed 3.4% of global greenhouse emissions in 2019 alone, according to the Environmental Protection Agency. Additionally, plastics have proved a major source of environmental pollution, with 85% of plastic waste accumulating in landfills or oceans—disrupting ecosystems, biodiversity and food chains.

“Plastic pollution is choking our planet, from landfills to oceans. With the support from this grant, we bring together cutting-edge tools in AI, machine learning and robotics automation to build a public, accessible foundation for sustainable materials development,” said Chen.

Working closely with industry partners, the research team aims to accelerate the transition toward renewable materials that are scalable, cost-effective, and environmentally responsible.

Although biopolymers are thought to match petrochemical plastics’ performance in mechanical resilience, strength and antimicrobial features, multiple scientific challenges have slowed down their advancement. A vast range of unexplored biopolymers have proved conventional research methods insufficient for the design task, and a lack of standardized preparation protocols undermines the accuracy of subsequent machine learning models. In order to replace petrochemical plastics, multiple characteristics would have to be met to match their performance, but recent studies have only focused on addressing one property at a time.

To address the scientific challenges, Chen, Li and Dutta’s team will accelerate the discovery and design of sustainable biopolymer nanocomposites with programmable properties. This project will consist of a comprehensive research framework that integrates biopolymer informatics, robotics systems, machine learning methodologies, and advanced simulation tools. Their design method represents a step forward in reducing fossil fuel reliance, lower carbon emissions, and providing a biodegradable pathway to mitigate microplastic pollution.

“Our system brings together AI, robotics, and simulations to quickly predict which biodegradable and sustainable materials will work best to accelerate real-world solutions to plastic pollution,” said Chen.

Published August 1, 2025