NC State Unveils AI-Driven Self-Driving Lab, Revolutionizing Material Discovery

In July 2025, researchers at North Carolina State University (NC State) unveiled a self-driving laboratory that integrates artificial intelligence (AI) with dynamic chemical experimentation, significantly accelerating the pace of materials discovery. This autonomous system conducts real-time, adaptive experiments, collecting data at a rate ten times faster than traditional methods. By continuously refining its experimental strategies based on incoming data, the lab reduces the time and resources required to identify optimal materials, promising rapid advancements in clean energy, electronics, and sustainability.

Self-driving laboratories (SDLs) are robotic platforms that combine machine learning and automation with chemical and materials sciences to expedite the discovery of new materials. These systems autonomously design, execute, and analyze experiments, iteratively refining their processes to achieve specific research goals. The integration of AI allows SDLs to make informed decisions on subsequent experiments based on real-time data, enhancing efficiency and reducing human intervention.

The NC State research team, led by Milad Abolhasani, ALCOA Professor of Chemical and Biomolecular Engineering, developed a self-driving lab that utilizes dynamic flow experiments. Unlike traditional steady-state flow experiments, which require the system to wait for chemical reactions to complete before analysis—a process that can take up to an hour per experiment—dynamic flow experiments involve continuously varying chemical mixtures monitored in real time. This approach allows the system to capture data every half second, providing a comprehensive view of the reaction as it occurs.

Abolhasani explained the significance of this advancement:

"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. 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."

This continuous operation enables the collection of at least ten times more data than previous techniques, dramatically expediting materials discovery research while reducing costs and environmental impact.

The development of this advanced SDL has far-reaching implications across various sectors:

• Clean Energy: Accelerated discovery of new materials can lead to more efficient solar panels, batteries, and other renewable energy technologies.
• Electronics: Rapid identification of novel semiconductors and conductive materials can enhance the performance and miniaturization of electronic devices.
• Sustainability: Efficient material discovery processes reduce waste and resource consumption, promoting more sustainable manufacturing practices.

Abolhasani emphasized the broader impact of this technology:

"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. This work brings that future one step closer."

This research was supported by the National Science Foundation under grants 1940959, 2315996, and 2420490, as well as the University of North Carolina Research Opportunities Initiative program. The collaborative effort included contributions from NC State researchers and Enrique A. López-Guajardo of Tecnologico de Monterrey.

The concept of SDLs has been evolving over the past decade, with NC State being a pioneer in this field. In 2020, Abolhasani's group developed "Artificial Chemist 2.0," an SDL capable of identifying and manufacturing quantum dots—semiconductor nanocrystals used in LEDs and solar panels—in less than an hour. The latest development represents a significant leap forward, offering unprecedented speed and efficiency in materials discovery.

Looking ahead, the integration of SDLs into mainstream research and industrial applications could revolutionize the pace of scientific discovery. By reducing the time-to-solution by 100 to 1,000 times, SDLs have the potential to address global challenges in energy, sustainability, and healthcare more rapidly and effectively.

NC State's development of a self-driving laboratory that integrates AI with dynamic chemical experimentation marks a significant leap forward in materials discovery. By dramatically increasing data collection rates and reducing resource consumption, this innovation paves the way for rapid advancements in various fields, promising a future where scientific breakthroughs are achieved with unprecedented speed and efficiency.