Collaborative research: dmref: NSF-nserc: data-driven materials by design for high performance organic mixed ionic-electronic conductive polymers and devices

The National Science Foundation Division of Materials Research awarded The University of Southern Mississippi $466,668 on July 1, 2026, for collaborative research developing data-driven materials-by-design strategies for high-performance organic mixed ionic-electronic conductive polymers and devices.

The project integrates molecular design, advanced characterization, device engineering, and artificial intelligence across U.S. and Canadian institutions to establish predictive tools that guide the development of organic electronic materials and reduce reliance on trial-and-error experimentation. These polymers combine electronic conductivity with ionic transport, enabling flexible, low-cost devices applicable to wearable sensors, bioelectronics, organic electrochemical transistors, and energy-efficient computing. The work also develops new curriculum modules in data-driven materials design, trains students in interdisciplinary data-enabled materials science and engineering, and conducts workshops highlighting artificial intelligence's role in future electronics.

Work is performed in Hattiesburg, Mississippi, with a period of performance from July 1, 2026, through June 30, 2030. The award is classified as a Project Grant under the NSF Mathematical and Physical Sciences program (CFDA 47.049), which supports advancement of mathematical and physical sciences research and strengthens the Nation's scientific enterprise.

Abstract

Non-technical description: organic electronic materials are integral to advanced applications, e.g., wearable sensors, bioelectronics, organic electrochemical transistors, and energy-efficient computing. Organic mixed ionic-electronic conducting polymers are particularly promising because they combine electronic conductivity with ionic transport, enabling flexible, low-cost devices compatible with biological and energy storage systems. However, improving their performance remains challenging because the relationships among molecular design, processing conditions, and device behavior are not yet well understood. This project develops a data-driven materials-by-design strategy to accelerate the design and discovery of high-performance polymers for next-generation electronic devices. By integrating molecular design, advanced characterization, device engineering, and artificial intelligence across U.S. And canadian institutions, the work establishes predictive tools that guide the development of organic electronic materials and reduce reliance on time-consuming trial-and-error experimentation. The project also trains students in interdisciplinary data-enabled materials science and engineering, develops new curriculum modules in data-driven materials design, and engages a broader audience through workshops highlighting the role of artificial intelligence in future electronics. These activities strengthen the workforce in advanced materials and digital manufacturing while aligning closely with the goals of the designing materials to revolutionize and engineer our future initiative, which aims to transform how advanced materials are discovered and engineered. Technical description: principal investigators establish an integrated framework to uncover structure?processing-property relationships in organic mixed ionic-electronic conducting polymers and their devices. Progress in organic electronics has traditionally relied on iterative synthesis-processing-characterization cycles, which can limit the discovery of high-performance materials. To address this challenge, researchers integrate modular polymer synthesis, high-throughput thin-film processing, device fabrication, multimodal characterization, and predictive modeling using artificial intelligence and machine learning. The framework emphasizes molecular engineering strategies for multicomponent polymer systems that integrate electronically conducting polymers with ION-transport materials, enabling independent control of ionic and electronic transport and systematic optimization of device performance. Comprehensive datasets linking molecular structure, processing parameters, morphology, and device metrics are generated to train multimodal predictive models that can identify design rules, forecast device behavior, and guide new materials discovery. Iterative feedback between synthesis, processing, modeling, and device testing creates a closed-loop materials-by-design platform spanning multiple institutions and both experimental and computational approaches. The results produce validated design principles, predictive computational tools, and curated datasets that accelerate the development of high-performance organic electronic materials and demonstrate a broadly applicable strategy for data-driven materials design and discovery. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.