# Collaborative Research: MFS-SPEED: Predictive Discovery of Sustainable Biopolymers via Multi-Attribute Descriptor System, Robotics/Machine Learning Workflow, and Open-Data Platform

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Iowa State University (IA) · $200,000

## Abstract

With this award, Professors Chen, Dutta, Li, and Curtzwiler of the University of Maryland and Iowa State University are studying how to leverage artificial intelligence to accelerate the discovery of high-performance and biodegradable polymer nanocomposites with tunable properties. The team will develop an integrated platform that combines robotic platforms, artificial intelligence, and materials chemistry to accelerate the process to identify, design, and test polymer composite films with customizable properties, such as mechanical strength, optical clarity, and moisture absorption. A public, open-access database and user-friendly interface will support broad engagement across scientific, industrial, and policy communities. In parallel, the project will foster workforce development through K-12 research internships, undergraduate mentorship, and the integration of findings into university curricula. These efforts will aim to cultivate the next generation of scientists and engineers equipped to lead innovation in artificial intelligence-accelerated materials discovery.
 
With this award, Professors Chen, Dutta, Li, and Curtzwiler of the University of Maryland and Iowa State University are studying how to integrate high-throughput robotic experimentation, explainable machine learning, and multiscale simulations to enable predictive design of biopolymer nanocomposites. The project will develop a multi-attribute descriptor framework to encode molecular structure, processing co

## Key facts

- **NSF award ID:** 2519438
- **Awardee organization:** Iowa State University (IA)
- **SAM.gov UEI:** DQDBM7FGJPC5
- **PI:** Greg Curtzwiler
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), CAS-Critical Aspects of Sustainability, Advanced Manufacturing, SusChEM, EXP PROG TO STIM COMP RES
- **Estimated total:** $200,000
- **Funds obligated:** $200,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2519438

## Citation

> US National Science Foundation, Award 2519438, Collaborative Research: MFS-SPEED: Predictive Discovery of Sustainable Biopolymers via Multi-Attribute Descriptor System, Robotics/Machine Learning Workflow, and Open-Data Platform. Retrieved via AI Analytics 2026-06-09 from https://api.ai-analytics.org/grant/nsf/2519438. Licensed CC0.

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