Polybot: AI and robotics unite to revolutionize polymer electronics research

Self-driving lab expedites the search for materials with a variety of uses.
Can Emir
Self-driving lab Polybot
Self-driving lab Polybot

Argonne National Laboratory 

A team of researchers at the U.S. Department of Energy’s Argonne National Laboratory has developed a new scientific tool called Polybot that combines artificial intelligence with robotics. This tool is set to revolutionize polymer electronics research by speeding up the discovery process of materials with multiple applications, from wearable biomedical devices to better batteries, according to a release.

Polymer electronics are the future of flexible electronics. They are efficient and sustainable, used to monitor health and treat certain diseases, among other things. However, the current methods used to prepare these polymers for electronics can take years of intense labor. To achieve targeted performance, there are an overwhelming number of potential tweaks, from spiking the fabrication recipe with different formulations to varying the processing conditions.

In steps Polybot

Polybot automates aspects of electronic polymer research and frees scientists' time to work on tasks only humans can accomplish. It combines the computational power of artificial intelligence (AI) with the automation possible with robotics. Polybot is one of several autonomous discovery labs starting up at Argonne and other research organizations. Their main purpose is to harness the power of AI and robotics to streamline experimental processes, save resources, and accelerate the pace of discovery.

The potential applications of Polybot extend far beyond biomedical devices. They include materials for computing devices with brain-like features and new sensors for monitoring climate change. They also include new solid electrolytes that would eliminate the current liquid electrolyte in lithium-ion batteries, making them less likely to catch fire.

How does Polybot work?

A typical experiment with Polybot begins by using AI and robots for different tasks. The automated system chooses a promising recipe for a polymer solution, prepares it, and prints it as a very thin film at a selected speed and temperature. The system then hardens this film for an optimal length of time and measures key features, such as thickness and uniformity, as a quality check. Next, it assembles multiple layers together and adds electrodes to form a device.

Polybot measures the device’s electrical performance, and all the relevant data are automatically recorded and analyzed with machine learning and passed to the AI component. The AI then directs what experiments to do next. Polybot can also respond to feedback provided by users and data from the scientific literature. “This is all done with minimal human intervention,” said Jie Xu, an assistant chemist at Argonne National Laboratory.

Polybot’s capabilities could be strengthened further to take full advantage of the Advanced Photon Source (APS), a DOE Office of Science user facility at Argonne. X-ray scattering analysis expands Polybot’s characterization down to the molecular level, revealing information about the orientation and packing of the molecules that can help speed up the search for the best materials with optimal performance. The team plans to utilize the Argonne Leadership Computing Facility to perform simulations that will provide better feedback to the AI.

In conclusion, the Polybot is a game-changer in the field of material science, specifically in the research of polymer electronics. With this tool, the discovery process can be accelerated from years to months, and the cost of complex projects can be reduced from millions to thousands of dollars.

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