Oct 24, 2024 07:33 PM
https://www.eurekalert.org/news-releases/1062176
INTRO: Science laboratories across disciplines—chemistry, biochemistry and materials science—are on the verge of a sweeping transformation as robotic automation and AI lead to faster and more precise experiments that unlock breakthroughs in fields like health, energy and electronics, according to UNC-Chapel Hill researchers in the paper, “Transforming Science Labs into Automated Factories of Discovery,” published in Science Robotics, the most prestigious journal covering robotics research.
“Today, the development of new molecules, materials and chemical systems requires intensive human effort,” said Dr. Ron Alterovitz, senior author of the paper and Lawrence Grossberg Distinguished Professor in the Department of Computer Science. “Scientists must design experiments, synthesize materials, analyze results and repeat the process until desired properties are achieved.”
This trial-and-error approach is time-consuming and labor-intensive, slowing the pace of discovery. Automation offers a solution. Robotic systems can perform experiments continuously without human fatigue, significantly speeding up research. Robots not only execute precise experimental steps with greater consistency than humans, they also reduce safety risks by handling hazardous substances. By automating routine tasks, scientists can focus on higher-level research questions, paving the way for faster breakthroughs in medicine, energy and sustainability.
“Robotics has the potential to turn our everyday science labs into automated ‘factories’ that accelerate discovery, but to do this, we need creative solutions to allow researchers and robots to collaborate in the same lab environment,” said Dr. James Cahoon, a co-author of the paper and chair of the Department of Chemistry. “With continued development, we expect robotics and automation will improve the speed, precision and reproducibility of experiments across diverse instruments and disciplines, generating the data that artificial intelligence systems can analyze to guide further experimentation.” (MORE - details, no ads)
INTRO: Science laboratories across disciplines—chemistry, biochemistry and materials science—are on the verge of a sweeping transformation as robotic automation and AI lead to faster and more precise experiments that unlock breakthroughs in fields like health, energy and electronics, according to UNC-Chapel Hill researchers in the paper, “Transforming Science Labs into Automated Factories of Discovery,” published in Science Robotics, the most prestigious journal covering robotics research.
“Today, the development of new molecules, materials and chemical systems requires intensive human effort,” said Dr. Ron Alterovitz, senior author of the paper and Lawrence Grossberg Distinguished Professor in the Department of Computer Science. “Scientists must design experiments, synthesize materials, analyze results and repeat the process until desired properties are achieved.”
This trial-and-error approach is time-consuming and labor-intensive, slowing the pace of discovery. Automation offers a solution. Robotic systems can perform experiments continuously without human fatigue, significantly speeding up research. Robots not only execute precise experimental steps with greater consistency than humans, they also reduce safety risks by handling hazardous substances. By automating routine tasks, scientists can focus on higher-level research questions, paving the way for faster breakthroughs in medicine, energy and sustainability.
“Robotics has the potential to turn our everyday science labs into automated ‘factories’ that accelerate discovery, but to do this, we need creative solutions to allow researchers and robots to collaborate in the same lab environment,” said Dr. James Cahoon, a co-author of the paper and chair of the Department of Chemistry. “With continued development, we expect robotics and automation will improve the speed, precision and reproducibility of experiments across diverse instruments and disciplines, generating the data that artificial intelligence systems can analyze to guide further experimentation.” (MORE - details, no ads)