A new $20 million National Science Foundation initiative will establish a network of AI-enabled automated laboratories across the United States, with the goal of accelerating chemical discovery and making cutting-edge research accessible to scientists nationwide. The project, called SPEED (Self-Driving Platforms for Experimental co-Design in Chemistry and Materials Science), will be led by North Carolina State University in partnership with the University of North Carolina at Chapel Hill and the Massachusetts Institute of Technology.

What Are Self-Driving Labs and How Do They Work?

Self-driving labs represent a fundamental shift in how scientific research happens. Rather than scientists manually conducting experiments one at a time, these facilities combine artificial intelligence with smart robotics to automate the experimental process. Human scientists still direct the research goals and questions, but the robots handle the actual laboratory procedures, running multiple experiments in parallel while machine learning algorithms predict which experiments are most likely to succeed.

The promise of this approach is dramatic. According to Alex Miller, a chemistry professor at UNC leading the Carolina team on the project, the combination of accelerated discovery and broad accessibility could be transformative. "The development of hardware and software tools that enable users that don't have access to advanced instrumentation to test their research ideas could be revolutionary," Miller explained. "The individual self-driving lab modules within SPEED can accelerate the process of moving from lead discovery to optimized outcome dramatically, by 100 times or more, by performing experiments in parallel and using machine learning methods to predict the most promising next set of experiments".

How Will These Labs Improve Energy Efficiency and Materials Science?

The SPEED project targets several practical applications that could have real-world impact. The Carolina team will focus on two major science drivers: enabling more efficient synthetic pathways to produce high-value specialty and fine chemicals, and accelerating the translation of next-generation materials into manufacturing for more energy-efficient digital displays and other technologies.

One of the key advantages of automating laboratory work is the ability to test many chemical reactions simultaneously. This parallel processing, combined with AI predictions about which experiments are most promising, dramatically reduces the time needed to move from discovering a promising chemical compound to optimizing it for real-world use. For energy-intensive industries like chemical manufacturing and materials science, this acceleration could lead to breakthroughs in creating more efficient processes and products.

Steps to Access and Control These Automated Laboratories Remotely

• Remote Access Interfaces: UNC's team will develop new access and control interfaces that allow researchers across the country to operate laboratory equipment without being physically present, democratizing access to expensive instrumentation.
• Broad Chemical Reaction Testing: The Carolina team will pilot a wide range of different chemical reactions within the automated systems, expanding the scope of what can be studied and optimized through self-driving labs.
• Robust Communication Tools: New software tools will enable stable, reliable remote communication with laboratory instruments, ensuring that researchers can monitor and adjust experiments in real time from anywhere.

The broader NSF initiative, called the Programmable Cloud Laboratories program, aims to enable remote control of automated laboratory instrumentation across a nationwide network. This means that a researcher at a small university or startup without access to expensive equipment could theoretically run experiments at one of these facilities, dramatically lowering barriers to innovation.

Why Does This Matter for AI and Energy Efficiency?

The intersection of AI and laboratory automation directly addresses one of the critical challenges facing the technology industry: energy efficiency. As artificial intelligence systems become more powerful and widespread, the energy required to train and run them continues to grow. Discovering new materials and chemical processes that are more energy-efficient is essential for making AI itself more sustainable. By accelerating the pace of materials discovery, self-driving labs could help identify better catalysts, more efficient semiconductors, and other innovations that reduce the energy footprint of AI systems and other technologies.

The four-year, $20 million award represents a significant investment in making this vision a reality. NC State's automated robotic experimentation capabilities, developed over the past several years, provide the foundation for this project. By bringing together expertise from three major research institutions, the SPEED lab aims to create a model that can be replicated and scaled across the country.

The implications extend beyond just chemistry and materials science. As these self-driving labs prove their value, they could reshape how scientific research is conducted across multiple disciplines, making breakthrough discoveries faster and more accessible to researchers who might otherwise lack the resources to pursue them.