The five-year award will support Ye’s efforts to develop free, open-source quantum chemistry tools that will help scientists understand and design better catalysts.
Hong-Zhou Ye, an assistant professor in the University of Maryland’s Department of Chemistry and Biochemistry with a joint appointment in the Institute for Physical Science and Technology, received a Faculty Early Career Development (CAREER) Award from the U.S. National Science Foundation (NSF). This is NSF’s most prestigious award for early-career scientists who have demonstrated notable and promising achievements in both teaching and research.
Over the next five years, Ye will receive nearly $700,000 to develop quantum chemistry tools for studying catalysts, materials that speed up chemical reactions or produce specific results. In addition, the award will support summer research opportunities for local high school students and computational chemistry workshops for UMD undergraduate and graduate students.
“I’m very happy to receive this award because it will fully support my students as we focus on this research,” Ye said. “It will also serve as an early introduction of this type of work to high school students who are interested in chemistry and acquiring experience in a lab.”
A quantum chemist, Ye develops computer-based tools that use the laws of quantum mechanics to predict how the atoms and electrons of catalysts behave during chemical reactions. His NSF CAREER project aims to speed up those simulations while maintaining accuracy and design new simulation tools tailored to metals, applying both to real-life scenarios.
“Chemistry is about transforming one substance into another, and it is the foundation for making almost everything we use in our lives—from the fabric in the clothes we wear every day to the fuels that we use to power our vehicles,” Ye explained. “Catalysts are an important part of making all of that possible, so studying how they work has implications across many fields."
Many of the most used industrial catalysts are transition metals, such as iron and cobalt. Simulating these metals on a computer is notoriously difficult. Their electrons are highly flexible and strongly influence each other, which lets the metal grab, rearrange and release molecules. This flexibility is what makes these metals good catalysts, but because every electron’s behavior depends on another, a computer can’t calculate them one at a time and must track them all together instead. The most accurate methods can take weeks of computing time and require enormous amounts of memory, so researchers often rely on faster methods that yield less certain results.
“Traditionally, people take these ‘shortcuts’ by making approximations in these calculations. But the problem is that once you use a lot of approximations, you don’t know whether the predictions you make will match the experiment,” Ye said. “Our goal is to reduce the computing time and power these methods require so that scientists can get reliable answers on ordinary computers, which typically have much less than a terabyte of computing power.”
To test the accuracy of his simulation tools, Ye will study the Fischer-Tropsch process, a decades-old industrial method that turns a mixture of carbon monoxide and hydrogen gas into liquid fuels like gasoline. The process relies on transition metal catalysts, but scientists still don’t fully understand what happens on their surfaces. Ye explained that more accurate simulations could reveal just how the reaction works—and that knowledge can help experimental chemists design catalysts that use less energy and produce more fuel.
“Solving that mystery can also lead to breakthroughs in other applications beyond the Fischer-Tropsch process and gasoline,” Ye said. “Scientists and manufacturers can design more efficient catalysts that take up fewer resources and create more desired product.”
Ye’s lab will make all the tools free and open source so anyone can download and use them. The tools will also be added to PySCF, a free chemistry software package used by researchers worldwide.
For Ye, accessibility to his work is an important theme of this project. In addition to opening his catalyst simulation work to the broader chemistry community, Ye mentors local high school students who learn introductory programming and quantum mechanics during summer internships in his lab.
“When equipped with the right tools, people with different backgrounds or funding can all have the chance to contribute meaningfully and solve the world’s problems together,” Ye said. “I hope to provide that to many people with this project.”
Ye joined UMD in 2024 after a postdoctoral fellowship at Columbia University. He earned his Ph.D. from MIT in 2020 and his B.S. from Peking University in 2015.
