Milad Abolhasani, recently named the R.J. Polge Term Professor for Excellence in Engineering in the Department of Chemical and Biomolecular Engineering at NC State, wants to solve real-world issues better and faster than ever before.

His work just received a major boost in the form of a four-year, $20 million National Science Foundation grant. The research initiative, dubbed Self-driving Platforms for Expedited Experimental co-Design in solution phase chemistry and materials science (SPEED), is part of the NSF Directorate of Technology, Innovation and Partnership’s Programmable Cloud Laboratories (PCL Test Bed) program. 

Abolhasani pioneers autonomous science platforms that combine AI, robotics and data-rich experimentation to accelerate the discovery and development of advanced materials and molecules. These platforms autonomously plan, execute, analyze and learn from experiments, enabling closed-loop discovery with minimal human intervention. 

“I became excited by the idea that we could combine human creativity with AI and robotics to dramatically accelerate discovery,” Abolhasani said. “Instead of doing science one experiment at a time, we can build laboratories that learn continuously, adapt intelligently and help researchers navigate chemical and materials spaces that are far too complex to explore by intuition alone.”

The broader goal is to transform how society discovers and manufactures the materials and molecules that power modern life, ranging from clean energy technologies and advanced semiconductors to sustainable catalysts, safer chemicals and next-generation medicines.

“Autonomous laboratories give us a new way to approach some of the hardest problems in science and engineering,” Abolhasani said. “They allow us to ask bigger questions, explore more possibilities and make better decisions faster. By creating autonomous and remotely accessible laboratories, we can make powerful discovery platforms available to more researchers, students, startups and institutions. In the long term, I believe self-driving labs can change not only what we discover, but who gets to participate in discovery.”

This interview with Abolhasani is part of an ongoing series in which we ask NC State faculty about their academic research, their motivations and their advice for students.

What is your area of expertise, and what motivated you to pursue it?

My research sits at the intersection of chemical engineering, AI, robotics and materials science. I focus on developing autonomous laboratories, sometimes called self-driving labs, that can combine automated experiments, real-time measurements and AI-guided decision-making to accelerate scientific discovery. 

What motivated me to pursue this field is a simple but urgent challenge: the traditional way we discover new materials and molecules is too slow for the problems society needs to solve. Whether we are talking about renewable energy, chemical manufacturing, advanced semiconductors or next-generation medicines, the number of possible materials, molecules and experimental conditions is enormous. In many cases, there are billions or even trillions of possibilities. Human intuition is essential, but intuition alone cannot explore those spaces efficiently.

Autonomous laboratories allow us to combine the creativity of scientists with the speed, precision and learning capacity of AI and robotics. That combination can help us move from slow, trial-and-error discovery toward a future where laboratories can continuously learn from every experiment and get smarter over time.

What kind of larger issues do you think your research could solve?

Autonomous science can help accelerate breakthroughs in areas that matter deeply to society, including renewable energy, low-carbon chemical manufacturing, advanced semiconductors, sustainable catalysts, quantum materials and safer, more efficient chemical processes. 

These are not abstract scientific challenges. They are directly connected to major societal needs, such as reducing carbon emissions, improving energy efficiency, strengthening domestic semiconductor innovation, developing safer materials, making chemical manufacturing more efficient and creating technologies that support a more sustainable future.

The key challenge is that many of these problems require us to discover materials or molecules with very specific combinations of properties. For example, we may need a catalyst that is active, selective, stable, inexpensive and scalable. Or we may need a semiconductor material that is efficient, durable, manufacturable and made from earth-abundant elements.

Finding those solutions requires exploring complex scientific landscapes. Autonomous laboratories can help researchers move through those landscapes much faster by designing experiments, learning from data and identifying promising directions that might otherwise be missed.

Why is university-based research important?

University-based research is essential because universities are uniquely positioned to take bold, long-term risks that can create entirely new fields. Industry is excellent at scaling technologies and solving product-driven problems, but universities are where many disruptive ideas first become possible.

In my area, building self-driving laboratories requires deep integration across disciplines: chemical engineering, robotics, AI, data science, materials science, analytical chemistry and systems engineering. Universities are one of the few environments where that kind of cross-disciplinary research can happen naturally.

Universities also train the next generation of scientists and engineers. That is critically important because the future of discovery will require people who can work across the physical, digital and biological worlds. At NC State, we are not just developing individual technologies. We are building an ecosystem where students learn how to combine physical experiments, AI models, robotics, real-time measurements and human judgment to solve complex problems. That educational mission is just as important as the research itself.

Describe your research in five words or less.

AI-powered robotic labs accelerating discovery.

What advice do you have for a student who wants to pursue similar research?

My advice is to become comfortable working across boundaries. The most exciting problems today do not fit neatly into one discipline. If you want to work in self-driving laboratories or AI for science, you should build a strong foundation in one core area, such as chemical engineering, chemistry, materials science, robotics, computer science or data science, but also stay curious about the others. Learn how to code with and without AI agents. Learn how to design experiments. Learn how to work with data. Learn how instruments and robots operate. Most importantly, learn how to ask good scientific questions. 

AI and robotics are powerful tools, but they are most impactful when guided by strong scientific intuition and creativity. The goal is not to replace scientists. The goal is to give scientists new capabilities and to help them explore larger spaces, test better hypotheses and discover solutions faster. 

I would also encourage students not to be intimidated by the complexity of this field. No one starts out as an expert in everything. The best researchers in this area are collaborative, curious, persistent and willing to learn new languages, whether that means the language of chemistry, machine learning, automation or engineering design. The future of science will belong to people who can connect these worlds.