“Self-Driving” Labs: How AI and Robotics Are Learning to Grow Materials on Their Own

Self-driving AI lab for autonomous material synthesis at the University of Chicago

Quantum Server Networks – Materials Science News Review

In a remarkable fusion of artificial intelligence, robotics, and experimental science, researchers at the University of Chicago Pritzker School of Molecular Engineering (PME) have built a fully autonomous laboratory system capable of growing thin films of metals entirely on its own. This “self-driving lab” can adjust temperature, composition, and timing parameters without human intervention—accelerating materials discovery and pointing toward a new era of AI-driven scientific exploration.

The research, published in npj Computational Materials (DOI: 10.1038/s41524-025-01805-0), demonstrates how machine learning can close the experimental loop—automating everything from data collection to decision-making—while achieving results that would normally take researchers weeks of repetitive manual work. The study was led by Assistant Professor Shuolong Yang and Ph.D. student Yuanlong “Bill” Zheng, and featured on Tech Xplore (November 2025).

From Manual Trial-and-Error to Autonomous Material Synthesis

Traditionally, the process of making thin metallic films for electronics, optics, and quantum technologies involves a time-consuming cycle of trial and error. Researchers must adjust the temperature, composition, and timing of the deposition process—known as physical vapor deposition (PVD)—to achieve precise material characteristics. Each iteration can take a day or more, and even tiny variations in humidity, pressure, or surface chemistry can alter results.

“We wanted to free researchers from the tedious, repetitive labor of setting up and tweaking these experiments,” explained Zheng. “Our system automates the entire loop—running experiments, measuring the results, and then feeding those results back into a machine-learning model that guides the next attempt.”

The team’s robotic setup integrates every step of the PVD process—from substrate handling and film growth to measurement and analysis—under the control of a machine-learning algorithm that continuously updates its model of the system’s behavior.

How the Self-Driving Lab Works

The system begins by forming a thin “calibration layer” of metal on each substrate. This step allows the algorithm to sense subtle differences in surface conditions, contamination levels, or gas composition that could affect growth quality. These parameters are then used to refine subsequent deposition attempts in real time, minimizing error and maximizing efficiency.

As the algorithm learns, it becomes increasingly adept at predicting which combination of deposition parameters—temperature, timing, and vapor composition—will yield the desired film properties. Within just a few runs, the AI-controlled lab achieves near-optimal results that would typically require human scientists weeks of careful tuning.

In one test, the system successfully produced silver films with specific optical characteristics in an average of only 2.3 attempts—demonstrating remarkable precision and adaptability. Over a few dozen trials, it covered the entire experimental parameter space, something a human team might take months to complete.

AI Meets Materials Science: Closing the Experimental Loop

The system is built around the principles of autonomous experimentation, an emerging concept in which AI algorithms guide the direction of laboratory research. In such setups, an experiment’s outcomes are analyzed in real time and immediately used to decide the next set of experiments—creating a closed feedback loop between data collection and decision-making.

“A researcher can tell the model what they want to achieve at the end, and the system designs the experiments to get there,” says Zheng. “It’s like giving the lab a goal—and letting it figure out how to reach it.”

By capturing every measurable variable and feeding it into a continually improving machine-learning framework, this approach helps overcome one of the biggest problems in experimental materials science: irreproducibility. Subtle environmental differences that once caused inconsistencies are now quantified, learned, and corrected automatically.

Building the Self-Driving System from Scratch

What makes this achievement even more impressive is its accessibility. The University of Chicago team built their autonomous system entirely in-house for under $100,000—a fraction of what similar commercial systems would cost. The hardware includes robotic manipulators, vacuum chambers, spectrometers, and cameras, all connected to an integrated AI control platform.

“This is just a prototype,” said Prof. Shuolong Yang, “but it shows how AI and robotics can transform not only how we make thin films, but how we approach materials discovery across the board.”

Next, the researchers plan to apply their self-driving lab framework to more complex materials systems, such as oxide heterostructures, quantum materials, and 2D semiconductors. The team envisions a future where similar systems could autonomously synthesize superconductors, catalysts, or even entirely new compounds designed by AI algorithms.

Toward the Future of Self-Driving Science

This work aligns with a broader movement toward “self-driving laboratories,” a concept that has gained momentum in the last decade. From Google DeepMind’s AlphaFold revolution in protein structure prediction to the Berkeley Lab A-Lab for automated material synthesis, AI is rapidly becoming an active participant in scientific discovery rather than just an analytical tool.

These systems are paving the way for what researchers call autonomous scientific exploration — a new paradigm in which intelligent machines design, execute, and analyze experiments faster and more reproducibly than humans. The implications span across materials science, chemistry, biology, and nanotechnology.

As Yang notes, “It points to a very intriguing futuristic mode of manufacturing.” Imagine a world where quantum materials, superconductors, or even flexible electronics are produced by AI-guided fabrication lines that learn and adapt over time — continuously optimizing themselves without human oversight.

Conclusion: The Rise of the Intelligent Laboratory

The self-driving lab at the University of Chicago marks an important milestone in merging artificial intelligence with physical experimentation. By combining robotics, data science, and materials engineering, it not only automates tedious processes but also redefines how scientific discovery itself can occur.

In the not-so-distant future, materials may be discovered, tested, and perfected by intelligent systems capable of reasoning, learning, and evolving. As AI takes its place at the lab bench, the boundaries of what’s possible in materials science may expand faster than ever before.

Original article source: Tech Xplore – “Self-driving lab learns to grow materials on its own” (November 2025).

This blog article was prepared with the help of AI technologies to enhance accessibility and public understanding of scientific research.

Announcement: The various articles on this blog have been read over 10,000 times in the past month by a large international audience of specialists in materials science and chemistry research.

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