Quantum materials are a class of exotic materials with special properties that are governed by rather than . Those properties 鈥� like , and unusual forms of magnetism 鈥� often originate in the tiny repeating patterns of atoms inside crystals, but through clever engineering they can be observed and controlled at a more human scale. Quantum materials are helping to power the quickly growing field of , and could find their way into future generations of energy-efficient electronics.听
Designing new materials from the atomic scale up, however, requires intense modeling and simulation. Some materials may appear ordinary when viewed as small clusters of atoms, yet reveal new and useful properties when their atomic building blocks repeat and interact over larger distances. Researchers must be able to accurately predict behaviors at large scales in order to find materials with practical applications 鈥� otherwise designing new materials is a slow and costly trial-and-error process.
In the past 50 years, supercomputers have helped materials scientists solve some of those thorny prediction problems, but two recent studies from the 乱伦社区 demonstrate how newer computing techniques can help researchers sniff out promising quantum materials to pursue. , published June 2 in the Proceedings of the National Academy of Sciences, shows how researchers can use artificial intelligence to simulate dozens of sheets of atoms stacked in intricate patterns, a process that produces complex and potentially useful quantum behaviors. , published June 8 in Nature Communications, shows how quantum computers can create a self-improving design loop by discovering new materials that could themselves be components of future quantum computers.听
鈥淲hat is exciting is that AI and quantum computing are beginning to change not just what problems we can solve, but how we do research,鈥� said , a UW associate professor of materials science and engineering and the senior author of both studies.
These two new tools 鈥� AI and quantum computing 鈥� are complementary in that they each excel at a different kind of simulation problem. With the right training, an AI model can act as a fast and relatively inexpensive surrogate of a supercomputer, extrapolating the behavior of huge material systems from a relatively small dataset. Cao and collaborators used this approach to stack virtual sheets of atoms on top of one another over and over 鈥� a process that created completely new phenomena that were absent on a smaller scale, but would have been impractical to model by traditional supercomputing. From there, researchers can try to make the most promising materials in the lab to prove out the simulations.
Quantum computers, on the other hand, are essentially powered by the same quantum phenomena 鈥� like entanglement 鈥� that Cao and other materials researchers want to study. Such phenomena can be difficult to simulate using traditional computers or AI systems, but quantum computers are naturally suited to the task. In the study, Cao and his team used a quantum computer to study an exotic phase of matter known as a .听
Moving forward, Cao and his team plan to further build out their datasets and eventually develop models that can simulate a much wider range of materials. They also hope to combine their AI and quantum computing systems into a more powerful and flexible hybrid tool.
鈥淭he next step is to bring these tools together,鈥� Cao said. 鈥淲e can use AI to guide quantum simulations, and quantum computers to generate new data and insights that improve AI models.鈥�
鈥淲e are at the start of a new era,鈥� said , UW professor and chair of materials science and engineering and co-author of both studies. 鈥淥ur 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.鈥�
was led by , a UW doctoral student of materials science and engineering. was led by , a UW doctoral student of physics. A complete list of authors is included with the studies.
The authors acknowledge the support of Amazon and the Department of Energy.
For more information, contact Cao at tingcao@uw.edu.