Three major universities are launching AI-powered automated laboratories that will let researchers across the country conduct advanced materials experiments remotely, potentially accelerating discovery timelines from years to weeks. The initiatives, funded by the National Science Foundation (NSF), represent a significant shift in how scientific research gets conducted, moving away from expensive, centralized facilities toward cloud-based platforms that democratize access to sophisticated equipment.

What Are Self-Driving Labs and Why Do They Matter?

Self-driving laboratories combine artificial intelligence, robotics, and digital simulation to automate the trial-and-error process that has traditionally defined materials science research. Instead of scientists manually adjusting variables like temperature, pressure, and chemical composition, AI agents recommend experiments, analyze results, and learn from both successes and failures.

Rice University received a $19.9 million NSF award to lead the "Revolutionizing AI-Driven Autonomous Experimentation for Next-Generation Semiconductor Synthesis" (READINESS) project, which will create a remote research platform for electronic and quantum materials synthesis. Meanwhile, NC State University secured a $20 million NSF grant to develop the "Self-Driving Platforms for Experimental co-Design in Chemistry and Materials Science" (SPEED) lab, with UNC-Chapel Hill and MIT as collaborators.

"This project will give researchers access to capabilities that have traditionally been available only in a handful of laboratories. By lowering those barriers, READINESS can accelerate discovery and expand who can participate in cutting-edge materials research," said David Sholl, executive vice president for research at Rice University.

David Sholl, Executive Vice President for Research, Rice University

How Do These AI Laboratories Actually Work?

• Digital Twins: Researchers simulate experiments in a virtual model of the laboratory before running them with physical equipment, reducing wasted time and materials on failed attempts.
• Robotic Automation: Automated synthesis equipment and robotic systems handle the physical work, adjusting conditions with precision while AI monitors outcomes and recommends next steps.
• Cloud-Based Access: Scientists propose experiments through a cloud interface, receive approval, and watch results in real time without needing to be physically present at the facility.
• Continuous Learning: The AI system learns from every experiment, successful or not, and uses machine learning to predict which experiments are most likely to yield promising results.

The READINESS project will initially focus on two-dimensional materials, oxide semiconductors, and diamond thin films, which have potential applications in faster electronics, lower-power computing, and quantum devices. The system will be housed at Rice's Ralph S. O'Connor Building for Engineering and Science, with partner sites at SUNY Polytechnic Institute and the University of Texas at Austin contributing equipment and expertise.

"Responsible AI should complement researchers' capabilities rather than replace their judgment. READINESS embodies this principle by combining automated systems with transparency, safeguards and human oversight at critical decision points," explained Luay Nakhleh, the William and Stephanie Sick Dean of Rice's George R. Brown School of Engineering and Computing.

Luay Nakhleh, Dean, George R. Brown School of Engineering and Computing, Rice University

How Could This Speed Up Scientific Discovery?

The acceleration potential is dramatic. According to researchers at UNC-Chapel Hill, self-driving labs could compress discovery timelines by 100 times or more by performing experiments in parallel and using machine learning to predict the most promising next set of experiments. This matters because developing new electronic and quantum materials traditionally requires researchers to manually adjust dozens of variables and wait weeks or months between experimental rounds.

Emerging research institutions, startups, and small to midsize companies often lack the expensive equipment and specialized staff necessary for producing advanced electronic and quantum materials. These new platforms aim to level the playing field by providing remote access to sophisticated laboratory systems that would otherwise cost millions of dollars to build and maintain.

"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. This combination of accelerated discovery and broad accessibility is really exciting," noted Alex Miller, professor of chemistry at UNC-Chapel Hill.

Alex Miller, Professor of Chemistry, UNC-Chapel Hill

What Happens to the Data and Results?

Both projects emphasize open science and data sharing. The READINESS platform will gather data from each experimental step, helping researchers link processing conditions with a material's structure and performance. The SPEED lab similarly aims to accelerate the translation of next-generation materials into manufacturing for more energy-efficient digital displays and other technologies.

These initiatives are part of a broader $380 million NSF investment in a national network of programmable cloud laboratories, with up to $20 million available in matching funds. The work also aligns with the Department of Energy's Genesis Mission, a national initiative using AI and advanced computing to accelerate scientific discovery.

Who Benefits From These New Laboratories?

Beyond immediate research applications, both projects include significant workforce development components. SUNY Polytechnic Institute will develop short courses and stackable credentials for workers in semiconductor manufacturing and laboratory automation. Students will gain hands-on experience in materials science, robotics, data management, and AI through graduate research, undergraduate internships, and K-12 outreach programs.

The Carolina team at UNC-Chapel Hill will be instrumental in implementing the SPEED project's science drivers on the automation infrastructure at NC State, developing new access and control interfaces and piloting a broad range of different chemical reactions. This collaborative approach ensures that the technology benefits not just elite research institutions but also smaller universities and companies that have historically lacked access to cutting-edge materials research capabilities.