As a nanoscientist, the University of North Carolina Greensboro’s Tetyana Ignatova focuses on materials at the smallest scales. Imagine the width of a human hair being split tens of thousands of times, and you get the idea.
But as she explores the properties of nanomaterials that are so thin that they are essentially two-dimensional, she thinks on a much grander scale. She envisions ways to apply these materials to innovate new technologies in areas as diverse as medicine, energy, environmental remediation, and manufacturing.
Now, Ignatova is hoping to move such technologies forward farther, faster, and more precisely by bringing together the disciplines of nanoscience and artificial intelligence (AI).
‘An absolutely new paradigm’
An associate professor in the Joint School of Nanoscience and Nanoengineering (JSNN), Ignatova served as academic co-chair of a national conference December 9-10 focusing on that convergence. The event, held in Alexandria, Virginia, brought together leading scientists from across the country who have received National Science Foundation (NSF) nanoscience and nanoengineering grants.
“Integrating AI and nanoscience can produce an absolutely new paradigm that will help us better understand the nature of nanomaterials and use them in technological applications that we did not even imagine before,” Ignatova says.
The conference included keynote speeches, panels, posters, program information sessions, discussions of research trends, and principal investigator meetings with NSF program directors.
“The meeting also provided opportunities for networking and exchange of ideas among scientists with similar interests,” Ignatova says. “Such interactions could spur future research collaboration and impact student training for years to come.”
Working with Students at the Leading Edge
Already, JSNN students are benefiting from being part of research related to AI and nanoscience. With Ignatova, they study what happens when two-dimensional materials – such as graphene, a single atomic layer of graphite – interact with the bulk materials of the three-dimensional world.
In October, recent graduate Jared Keith Averitt published a paper, titled “Efficient High-Throughput Method Utilizing Neural Network Potentials to Calculate Interaction Energies, Validated by Clean Transfer Experiment of CVD Graphene with Polymer Mixtures,” in the peer-reviewed journal Carbon.
Averitt now works at Los Alamos National Laboratory on research at the intersection of nanoscience and artificial intelligence, and he credits Ignatova as a “lifelong academic mentor and guiding force.”
“What stands out most about Dr. Ignatova is her passion for science and her genuine care for her students,” he says. “She had an innate ability to see the potential in others and push them toward achievements they didn’t think possible. She believed that science was not just about making discoveries but about building people.”
Motivational Respect
From Ignatova’s perspective, the respect is mutual. She sees training the next generation of nanoscientists as the most important aspect of her work and often finds herself drawing inspiration from them.
In fact, she explains, Averitt is one of the students who inspired her to begin incorporating AI into her nanoscience research.
Stuck at home during the COVID-19 pandemic, these students couldn’t do lab work, Ignatova says, and “shifted their interest towards computer methods and advanced data analysis, programming, and coding.”
“They were highly excited about artificial intelligence and very easily catching the new technology vibes, implementing the research, working hard,” she says. “They motivated me, and now this is a very important direction in my research.”
Fueling Nanoscience Innovation
With her students, Ignatova learned how powerful AI can be in speeding nanoscience discoveries.
“Previously, we would need to run optical experiments and then spend a couple of weeks doing data analysis. But by using machine learning algorithms, we boosted it to just a couple of days,” she says.
Ignatova is optimistic that as more scientists get involved in research at the intersection of artificial intelligence and nanoscience, technology will quickly advance, with benefits for people and the environment.
She hopes that the NSF Nano-AI Convergence conference leads to the discovery of new nanomaterials, the design of more accurate and efficient nanodevices, and the development of nanosystems with useful capabilities and without negative consequences.
Ignatova also wants conference participants to come away from the conference excited about the possibilities ahead.
“I hope the conference will help inform the direction of related research in the coming decade,” she adds, “emphasizing what is extremely important and where we should move next.”
Story written by Dee Shore, AMBCopy LLC
Photography provided by Sean Norona, University Communications
