There are few scientists who would describe condensed matter physics—a branch of physics that studies the behavior of solids—as “simple.” But to Giulia Galli, Liew Family Professor of Molecular Engineering, it’s less complex than what she now works on at the University of Chicago.

“Problems like water and energy are much more complicated than what I was trained for in condensed matter physics,” says Galli, a theorist who uses computational models to figure out the behavior of molecules and materials. “All of my work is driven by problems.”

The focus of Galli’s research is to predict molecular behavior and understand how to harness it to improve technology—particularly in the areas of purifying water, speeding up computation and sensing with quantum technology, and perfecting renewable energy technology.

“Essentially, we predict how atoms arrange themselves,” says Galli. “We do this by developing theoretical algorithms and powerful codes and simulations in order to understand the quantum mechanics at play in a given material.”

For example, her group can use theory to predict which of several materials will make a cheaper solar cell or to suggest a new configuration for a quantum bit made from electron spins. “Energy and water are incredibly important problems—even a small improvement from your science can have a huge impact,” she says.

Galli, who also heads the Midwest Integrated Center for Computational Materials, has earned international recognition for her work. She recently received the Feynman Theory Prize, an annual honor highlighting extraordinary work in harnessing quantum mechanics for the public interest. It was her fourth major award this year.

“It is not difficult to understand why Giulia has been recognized as a scientific leader by a diverse set of scientific organizations,” says Matthew Tirrell, the Robert A. Millikan Distinguished Service Professor and dean of the Pritzker School of Molecular Engineering. “She wields powerful and versatile computational tools that she has deployed to learn about many important scientific matters.”

Quantum mechanics describes the rules of atomic behavior at incredibly tiny scales—a world full of the unexpected, which Galli seeks to explain using computer codes. But the challenge of modeling the interactions between hundreds of thousands of atoms in a material is a Herculean task. Often she uses the University’s Research Computing Center, but for more complex simulations, her team turns to the extremely powerful supercomputers at the UChicago-affiliated Argonne National Laboratory, where Galli has a joint appointment.

The simulations may take months, depending on the problem; in fact, Galli’s group is constantly running simulations on as many machines as they can: “We probably have 15 projects running right now,” she says.

Since joining Pritzker Molecular Engineering from the University of California, Davis, in 2014, she’s been able to work much more closely with scientists on the experimental side, creating a loop where their experiments explore and validate her theoretical predictions, and her insights suggest new avenues for experimentation.

One frequent collaborator is David Awschalom, Liew Family Professor of Molecular Engineering and director of the Chicago Quantum Exchange. Last year the pair devised a new way of connecting quantum states of matter using sound waves. Galli’s team developed a computer model that helped Awschalom’s team better understand what they were observing in their experiments.

Recently she’s become interested in addressing a problem in science known as the data reproducibility crisis. All good experiments and calculations have to be able to produce consistent results, no matter who’s doing the experiment or carrying out the simulation—but as simulations grow more complex and the amount of data skyrockets, it becomes harder for other scientists to check their peers’ work.

Galli began providing links for interested parties to download the data and codes from her work, but that was only a local solution. To address the problem on a larger scale, she created an open-source tool called Qresp that provides a framework for researchers to share their data and workflows, so that others can see how the results were reached—and try to poke holes in them.

She sees this as essential for science and for scientists. “The job of a good scientist is to constantly doubt your answers,” Galli says. “The minute you get results, you have to think about how to validate them. How to find a different way to evaluate them. To push and challenge yourself. To do what you don’t yet know how to do.”