Associate Professor Sara Kadkhodaei receives NSF CAREER Award to investigate defects in materials

CME Associate Professor Sara Kadkhodaei has been awarded the prestigious NSF CAREER Award.

“Receiving the NSF CAREER Award is incredibly exciting for me because this project reflects a research direction I have been deeply committed to and developing over the past several years. Having this research problem recognized as important through the award was especially meaningful,” said Kadkhodaei, director of the Computational Materials Research Laboratory.

The research, “First-Principles Thermodynamic Models for Defect Phase Diagrams,” investigates defects in materials, which play a fundamental role in determining their behavior.

“None of the materials we use in real technologies are perfectly ordered at the atomic scale—and that is not necessarily a bad thing. In many cases, defects are actually responsible for useful properties and can be used as design tools if we understand and control them,” she said.

She is focusing on the population of defects and wants to understand what type form, how many form, and how those populations change when the material is exposed to different temperatures, chemical environments, or other operating conditions.

“Those changes can strongly influence the functional properties of the material. Because this is such a widespread phenomenon across many classes of materials, having reliable predictive tools for defect thermodynamics is extremely important,” she said.

What makes this project different is that Kadkhodaei is trying to move beyond models that depend heavily on existing empirical data. Instead, she wants to predict defect behavior starting from the fundamental physics and chemistry of the material—how atoms bond, how electrons respond when a defect forms, and how defects interact with one another.

“The project is connecting two different worlds: the atomic-scale physics and chemistry of individual defects and the collective behavior of enormous populations of defects in a real material. Bridging those scales can give us a much more predictive way to understand and ultimately design materials through their defects,” she said.

The research has the potential to enable better, more efficient materials design. Many technologies—from electronics and energy systems to sensors and optical devices—depend on the ability to understand and control defects in materials. Much of that optimization relies on experiments and empirical models, often requiring large amounts of data and numerous time-consuming tests under varying conditions.

“The transformative potential of this project comes from developing a first-principles predictive framework, meaning that the predictions start from the fundamental physics of atoms and electrons rather than depending primarily on existing experimental data. If we can use a relatively small amount of fundamental atomic-scale information to predict defect behavior, we can greatly reduce trial and error in materials development,” she said.

This kind of predictive capability can help experimental researchers identify promising materials and the conditions required to achieve desired properties before conducting extensive experiments. Ultimately, it can shorten the time required and reduce the cost of designing and optimizing new materials.

“Because defects are important across such a wide range of technologies, improving our ability to predict and control them can have a broad impact on future materials innovation,” she said.

Kadkhodaei also plans to emphasize the integration of research and education by offering UIC students the opportunity to be directly involved in the research and the educational components of this project.

“I care deeply about research, but education—particularly graduate education—is also a very important part of what I do. This award gives me the opportunity to help fill what I see as a gap in materials education by creating resources that help students connect the fundamental physics and chemistry of materials with more practical thermodynamic modeling and materials design,” she said. “Graduate and undergraduate students will contribute to developing and applying the new computational methods, giving them experience at the intersection of materials science, physics of defects, thermodynamics, computation, data science, and artificial intelligence.”