Researchers at Argonne National Laboratory are using Polybot, an AI-driven automated materials laboratory, to develop high-conductivity, low-defect electronic polymer thin films.
Image Credit: Anggalih Prasetya/Shutterstock.com
Plastic that conducts electricity may seem unlikely, but a unique class of materials known as "electronic polymers" combines the flexibility of plastic with the electrical properties of metals. These materials have significant potential for applications in wearable devices, printable electronics, and advanced energy storage systems.
However, producing thin films from electronic polymers has traditionally been a complex challenge. Achieving the right balance of physical and electronic properties requires extensive fine-tuning. To address this, researchers at the US Department of Energy’s (DOE) Argonne National Laboratory have turned to artificial intelligence (AI).
Using Polybot, an AI-driven, automated materials laboratory, scientists are exploring new processing methods to produce high-quality films. Located at the Center for Nanoscale Materials, a DOE Office of Science user facility at Argonne, Polybot represents a significant advancement in autonomous discovery—an approach that integrates robotics with AI to accelerate scientific progress.
Polybot operates on its own, with a robot running the experiments based on AI-driven decisions. We are creating a method that highlights how AI and automation can transform chemical engineering and materials science.
Jie Xu, Scientist, Argonne National Laboratory
The team used Polybot to help tackle some of the key challenges associated with electronic polymer processing. For instance, the final properties of these materials are impacted by a complex production history. Nearly a million possible combinations in the fabrication process can affect the final properties of the films—far too many possibilities for humans to test.
We faced limited resources and had little knowledge about the vast processing options. Using AI-guided exploration and statistical methods, Polybot efficiently gathered reliable data, helping us find thin film processing conditions that met several material goals.
Henry Chan, Computational Materials Scientist, Argonne National Laboratory
Polybot enabled researchers to optimize two critical properties simultaneously: conductivity and coating defects. Enhancing conductivity while minimizing defects improves device reliability and enhances electrical performance.
This fully automated platform streamlines formulation, coating, and post-processing, making experimentation and data collection significantly more efficient. As a result, the team produced thin films with conductivity levels comparable to the highest industry standards. They also developed precise “recipes” for large-scale production of these films.
According to Argonne research scientist Aikaterini Vriza, one of the project's key achievements was the integration of advanced computer programs capable of processing and analyzing images.
These programs not only helped us perform experiments and create films, but they also allowed us to capture images and evaluate the quality of the films. This information was crucial to our efforts to produce high-quality, highly conductive films.
Aikaterini Vriza, Research Scientist, Argonne National Laboratory
As well as making films, the researchers also collected valuable data, which they plan to share with the scientific community through a database. This adds significant value to their work.
Vriza added, “Data is critical. We support open-source research, and by sharing this data, we hope to motivate the community to contribute to, test, and improve our methodology.”
The impact of this work extends beyond developing electronic polymers in the lab—it also lays the groundwork for large-scale production. The recipes and guidelines from this research offer valuable insights for scientists and manufacturers looking to explore electronic polymers for a wide range of applications.
Xu stated, “This project is just the beginning. We have shown that our approach works. Next, we want to dive deeper into using AI and automated processes to tackle more real-world challenges and help discover new materials.”
This research leveraged the Materials Engineering Research Facility at Argonne for electronic printing support and the National Synchrotron Light Source II at DOE’s Brookhaven National Laboratory for wide-angle X-ray scattering characterization.
Journal Reference:
Wang, C. et. al. (2025) Autonomous platform for solution processing of electronic polymers. Nature Communications. doi.org/10.1038/s41467-024-55655-3