Insider Brief
- The University of Maryland is funding a research project that will use quantum computing and AI to study new materials for cancer detection and treatment.
- The project will focus on single-atom catalysts, which may help improve cancer therapies by altering the tumor environment and enhancing treatment response.
- Researchers plan to release datasets and computational tools to support broader study of quantum- and AI-driven cancer materials discovery.
The University of Maryland is backing a new effort to use quantum computing and artificial intelligence to speed the search for better ways to detect and treat cancer.
The project is one of 11 research efforts funded through the university’s Grand Challenges Grants Program, a three-year initiative that will provide nearly $15 million for work aimed at major social, health and technology problems, according to a university press release. The cancer project is supported as a team grant and brings together researchers from the A. James Clark School of Engineering and the College of Computer, Mathematical, and Natural Sciences.
The research will focus on single-atom catalysts, a class of materials made by placing isolated metal atoms on a supporting surface, according to the university. These materials can drive chemical reactions with high precision and have drawn interest as possible tools in cancer treatment and detection. In cancer therapy, they may help alter the tumor microenvironment, the local area around cancer cells that can influence how tumors grow, resist treatment and respond to therapy.
The University of Maryland team — which includes which includes Teng Li, Keystone Professor, Department of Mechanical Engineering, Maryland Energy Innovation Institute; Lianping Wu, assistant research professor of mechanical engineering and Xiaodi Wu, associate professor of computer science — plans to combine quantum computing and machine learning to design these catalysts more quickly and more accurately than conventional methods allow, according to the project summary. The goal is to build a predictive framework that can identify promising materials for cancer detection and therapy before they move into laboratory testing.
Cancer remains one of the world’s leading causes of death, the researchers point out. The disease is responsible for nearly 10 million deaths each year globally, or about one in six deaths, according to figures cited by the university. In the U.S., more than 2 million new cancer cases and about 618,000 cancer-related deaths were expected in 2025.
The project is aimed at two persistent problems in cancer care. Many cancers are still not found early enough, when tumors are often smaller, less aggressive and more likely to respond to treatment. Current therapies, including chemotherapy and radiation, can also harm healthy tissue while attacking cancer cells. Over time, tumors may develop defenses that make treatments less effective.
A Search for Better Materials
Single-atom catalysts offer one possible path toward more targeted cancer tools, according to the university. Because these materials operate at the atomic scale, small changes in their structure can affect how they interact with chemical compounds in tumors or treatment environments.
Researchers have studied single-atom catalysts in cell and animal models, but the field remains largely preclinical. That means the materials are still far from routine use in hospitals and must undergo extensive testing before they can be considered for clinical applications.
Designing these catalysts remains difficult. Researchers must account for how atoms, electrons and chemical reactions behave in complex environments. Traditional trial-and-error discovery can be slow and expensive, especially when scientists must sort through large numbers of possible material structures.
The University of Maryland team plans to use quantum computing to model parts of this process that are hard for ordinary computers to capture. Quantum computers use the rules of quantum physics to process information to perform calculations on certain problems that vex classical computers. While today’s machines are still limited, researchers are exploring whether they can help simulate complex materials and chemical reactions more accurately than classical systems in certain cases.
According to the project summary, quantum computing could help produce more reliable databases by simulating electronic structures and catalytic reaction pathways that are difficult for conventional methods. Those databases would then support machine learning models, which are AI systems trained to find patterns in data.
The machine learning models would be used to search through millions of possible single-atom catalyst configurations and predict which structures are most likely to show strong catalytic activity for cancer detection or therapy, according to the university.
AI and Quantum as Discovery Tools
The project reflects a broader move in biomedical research toward computational discovery, in which scientists use advanced computing to narrow the field of possible drugs, materials or treatment tools before moving to experiments. The approach does not replace laboratory or clinical testing, but it can help researchers decide which candidates are worth pursuing.
For cancer research, the promise is a faster route from material design to experimental validation. If successful, the Maryland team’s method could reduce reliance on costly trial-and-error studies and provide a more systematic way to develop catalytic materials that enhance existing treatments, including radiation therapy.
The project also sits at the intersection of public health, AI, quantum computing, materials science and cancer medicine. The university said the work is designed to strengthen collaboration among computational scientists, materials researchers and cancer experts.
The team also plans to release benchmark datasets and reproducible computational tools, according to the project summary. That open-science approach is intended to allow other research groups to test, compare and build on the framework.
The university said results from the project will be shared through research publications, interdisciplinary conferences and public release of data and tools.
Potential Impact
The research is still early and is focused on discovery rather than immediate clinical use. Any catalyst designed through the project would need to be tested in laboratory studies, animal models and, eventually, human trials before it could become part of cancer care.
Still, the university frames the project as a way to improve the front end of cancer-therapy development. By using quantum simulations and AI models to identify stronger candidates earlier, researchers may be able to cut the time and cost needed to find useful therapeutic materials.
The project could also have implications beyond Maryland. Nationally, it supports U.S. efforts to apply advanced computing and AI to health care and materials discovery. Globally, the university said catalytic platforms that improve existing treatments could be especially valuable in regions where access to advanced medical infrastructure is limited.
The university added that the first round of Grand Challenges Grants committed $30 million to 50 projects spanning every college and school — the largest investment of its kind in UMD’s history, resulting in an additional $55 million in external funding.
“The inaugural program demonstrated extraordinary impact due to the breadth of expertise and collaborative spirit across our research enterprise,” Senior Vice President and Provost Jennifer King Rice and Vice President for Research Patrick O’Shea said in an email to the campus community, according to the news release. “Through Grand Challenges 1.0, faculty developed innovative approaches to issues from climate resilience to food insecurity to educational equity and more, strengthening partnerships across disciplines, engaging students in new opportunities, and positioning the university for greater external funding, scholarly impact and public engagement.”
