Robotic automation, AI will accelerate scientific progress in scientific laboratories

Scientific laboratories across disciplines—chemistry, biochemistry, and materials science—are on the brink of sweeping transformation as robotic automation and AI lead to faster, more accurate experiments that unlock advances in fields such as health, energy, and electronics. .

That’s according to UNC-Chapel Hill researchers in a paper titled “Transforming Science Labs into Automated Discovery Factories,” published in Scientific robotics.

“Today, the development of new molecules, materials and chemical systems requires intensive human effort,” said Dr. Ron Alterovitz, senior author of the paper and the Lawrence Grossberg Distinguished Professor in the Department of Computer Science. “Scientists must design experiments, synthesize materials, analyze the results, and repeat the process until the desired properties are achieved.”

This trial-and-error approach is time- and labor-intensive, slowing the pace of discovery. Automation offers a solution. Robotic systems can perform continuous experiments 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 advances in medicine, energy and sustainability.

“Robotics has the potential to turn our everyday science labs into automated ‘factories’ that accelerate discovery, but to do that, 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 head of the Department of Chemistry.

“With continued development, we expect robotics and automation to improve the speed, accuracy and reproducibility of experiments across instruments and disciplines, generating data that artificial intelligence systems can analyze to guide further experimentation.”

The researchers defined five levels of laboratory automation to illustrate how automation can evolve in scientific laboratories:

• Assistive automation (A1): At this level, individual tasks, such as liquid handling, are automated while humans handle most of the work.
• Partial automation (A2): Robots perform multiple sequential steps, with humans responsible for setup and supervision.
• Conditional automation (A3): Robots manage all experimental processes, although human intervention is required when unexpected events occur.
• High automation (A4): Robots perform experiments independently, setting up equipment and reacting to unusual conditions autonomously.
• Full Automation (A5): In this final stage, robots and AI systems operate with full autonomy, including self-maintenance and security management.

The levels of automation determined by researchers can be used to assess progress in the field, help establish appropriate safety protocols, and set goals for future research in both the fields of science and robotics. Although lower levels of automation are common today, achieving high and full automation is a research challenge that will require robots capable of operating in diverse laboratory environments, handling complex tasks, and interacting with humans and other automation systems without problems.

Artificial intelligence plays a key role in advancing automation beyond physical tasks. AI can analyze vast data sets generated by experiments, identify patterns and suggest new compounds or research directions. Integrating AI into the laboratory workflow will allow laboratories to automate the entire research cycle—from designing experiments to synthesizing materials and analyzing results.

In AI-driven labs, the traditional Design-Make-Test-Analyze (DMTA) cycle can become fully autonomous. AI can determine which experiments to run, make real-time adjustments, and continuously improve the research process. While AI systems have shown early success in tasks such as predicting chemical reactions and optimizing synthesis routes, researchers caution that AI must be carefully monitored to avoid risks, such as the accidental creation of hazardous materials.

The transition to automated laboratories presents significant technical and logistical challenges. Laboratories vary widely in their structures, ranging from single-process laboratories to large multi-room facilities. Developing flexible automation systems that operate in diverse environments will require mobile robots capable of transporting objects and performing tasks across multiple stations.

Training scientists to work with advanced automation systems is equally important. Researchers will not only need to develop expertise in their scientific fields, but also understand robotics, data science and AI capabilities to accelerate their research. Educating the next generation of scientists to collaborate with engineers and computer scientists will be essential to realizing the full potential of automated laboratories.

“The integration of robotics and AI is poised to revolutionize scientific laboratories,” said Angelos Angelopoulos, a co-author of the paper and research assistant in the Computational Robotics Group of Dr. Alterovitz. “By automating routine tasks and accelerating experimentation, there is great potential for creating an environment where breakthroughs happen faster, safer and more reliably than ever before.”