Researchers at the University of Washington are using artificial intelligence and quantum computing to simulate and discover new quantum materials, bypassing the slow trial-and-error process of traditional materials design. The two studies, published in June 2026, demonstrate how these technologies can model complex atomic behaviors at scale, potentially accelerating the development of energy-efficient electronics and quantum computer components.
Simulating complex atomic stacks
Designing materials from the atomic scale up requires intense modeling. Small clusters of atoms often behave differently than when their building blocks repeat over larger distances. Supercomputers have addressed some of these prediction problems over the past 50 years, but newer techniques are proving faster. A study published June 2 in the Proceedings of the National Academy of Sciences shows researchers using AI to simulate dozens of molybdenum ditelluride crystal sheets stacked in intricate patterns. This process produces complex quantum behaviors absent on a smaller scale, acting as a fast, inexpensive surrogate for traditional supercomputing.
This approach to data modeling and scientific discovery mirrors the methodologies covered in an AI Learning Path for Research Scientists. Once researchers identify promising virtual materials, they can attempt to synthesize them in the lab to validate the simulations.
Quantum computers studying quantum phenomena
While AI extrapolates large systems from small datasets, quantum computers are naturally suited to simulate the quantum phenomena researchers want to study, such as entanglement. A second study, published June 8 in Nature Communications, details how a quantum computer created a self-improving design loop by discovering new materials. The team used the quantum computer to study an exotic phase of matter known as a Laughlin state.
"What is exciting is that AI and quantum computing are beginning to change not just what problems we can solve, but how we do research," said Ting Cao, a UW associate professor of materials science and engineering and senior author of both studies.
Cao and his team plan to build out their datasets and develop models that simulate a wider range of materials. This integration of computational methods highlights the growing role of AI for Science & Research in modern laboratory workflows.
"The next step is to bring these tools together," Cao said. "We can use AI to guide quantum simulations, and quantum computers to generate new data and insights that improve AI models."
Why this matters for science and research professionals
The ability to simulate complex quantum materials without relying solely on physical trial-and-error reduces development time and cost. For professionals in scientific research, this shift means computational modeling is becoming a primary discovery tool rather than just a validation step. Researchers can now use AI to guide quantum simulations, while quantum computers generate new data to refine those same AI models.
"We are at the start of a new era," said Di Xiao, UW professor and chair of materials science and engineering and co-author of both studies. "Our field is fundamentally changing. Things that were literally impossible a couple of years ago are now becoming routine. And we are only beginning to see what AI and quantum computing will make possible for quantum materials."
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