This AI-powered lab runs itself—and discovers new materials 10x faster

Self-driving laboratories are robotic platforms that combine machine learning and automation with chemical and materials sciences to discover materials more quickly. The automated process allows machine-learning algorithms to make use of data from each experiment when predicting which experiment to conduct next to achieve whatever goal was programmed into the system.

"Imagine if scientists could discover breakthrough materials for clean energy, new electronics, or sustainable chemicals in days instead of years, using just a fraction of the materials and generating far less waste than the status quo," says Milad Abolhasani, corresponding author of a paper on the work and ALCOA Professor of Chemical and Biomolecular Engineering at North Carolina State University. "This work brings that future one step closer."

Until now, self-driving labs utilizing continuous flow reactors have relied on steady-state flow experiments. In these experiments, different precursors are mixed together and chemical reactions take place, while continuously flowing in a microchannel. The resulting product is then characterized by a suite of sensors once the reaction is complete.

"This established approach to self-driving labs has had a dramatic impact on materials discovery," Abolhasani says. "It allows us to identify promising material candidates for specific applications in a few months or weeks, rather than years, while reducing both costs and the environmental impact of the work. However, there was still room for improvement."

Steady-state flow experiments require the self-driving lab to wait for the chemical reaction to take place before characterizing the resulting material. That means the system sits idle while the reactions take place, which can take up to an hour per experiment.

"We've now created a self-driving lab that makes use of dynamic flow experiments, where chemical mixtures are continuously varied through the system and are monitored in real time," Abolhasani says. "In other words, rather than running separate samples through the system and testing them one at a time after reaching steady-state, we've created a system that essentially never stops running. The sample is moving continuously through the system and, because the system never stops characterizing the sample, we can capture data on what is taking place in the sample every half second.

"For example, instead of having one data point about what the experiment produces after 10 seconds of reaction time, we have 20 data points - one after 0.5 seconds of reaction time, one after 1 second of reaction time, and so on. It's like switching from a single snapshot to a full movie of the reaction as it happens. Instead of waiting around for each experiment to finish, our system is always running, always learning."

Collecting this much additional data has a big impact on the performance of the self-driving lab.

"The most important part of any self-driving lab is the machine-learning algorithm the system uses to predict which experiment it should conduct next," Abolhasani says. "This streaming-data approach allows the self-driving lab's machine-learning brain to make smarter, faster decisions, honing in on optimal materials and processes in a fraction of the time. That's because the more high-quality experimental data the algorithm receives, the more accurate its predictions become, and the faster it can solve a problem. This has the added benefit of reducing the amount of chemicals needed to arrive at a solution."

In this work, the researchers found the self-driving lab that incorporated a dynamic flow system generated at least 10 times more data than self-driving labs that used steady-state flow experiments over the same period of time, and was able to identify the best material candidates on the very first try after training.

"This breakthrough isn't just about speed," Abolhasani says. "By reducing the number of experiments needed, the system dramatically cuts down on chemical use and waste, advancing more sustainable research practices.

"The future of materials discovery is not just about how fast we can go, it's also about how responsibly we get there," Abolhasani says. "Our approach means fewer chemicals, less waste, and faster solutions for society's toughest challenges."

The paper, "Flow-Driven Data Intensification to Accelerate Autonomous Materials Discovery," will be published July 14 in the journal Nature Chemical Engineering. Co-lead authors of the paper are Fernando Delgado-Licona, a Ph.D. student at NC State; Abdulrahman Alsaiari, a master's student at NC State; and Hannah Dickerson, a former undergraduate at NC State. The paper was co-authored by Philip Klem, an undergraduate at NC State; Arup Ghorai, a former postdoctoral researcher at NC State; Richard Canty and Jeffrey Bennett, current postdoctoral researchers at NC State; Pragyan Jha, Nikolai Mukhin, Junbin Li and Sina Sadeghi, Ph.D. students at NC State; Fazel Bateni, a former Ph.D. student at NC State; and Enrique A. López-Guajardo of Tecnologico de Monterrey.

This work was done with support from the National Science Foundation under grants 1940959, 2315996 and 2420490; and from the University of North Carolina Research Opportunities Initiative program.