Researchers using the Aurora supercomputer at the U.S. Department of Energy’s Argonne National Laboratory have achieved the largest-ever simulations of light interacting with quantum materials. Their groundbreaking work could help drive the discovery of new materials for high-speed, low-power electronics and computing technologies.
Light-matter interactions in quantum materials are key to meeting the growing energy demands of AI and next-generation computing. Because these interactions are difficult to observe directly, the researchers rely on large-scale simulations to understand how light alters material properties at the atomic level.
Built in partnership with Intel and Hewlett Packard Enterprise, Aurora is one of the largest and most powerful computers in the world. The system allowed the USC-led team to simulate light-matter dynamics at a massive scale with the aid of AI. They were able to perform simulations of materials with more than one trillion atoms. Their runs on Aurora achieved speedups thousands of times faster than earlier methods and reached a sustained performance of 1.87 exaflops. One exaflops equals one quintillion, or a billion billion, calculations per second. Aurora, one of the first exascale systems, is operated by the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility.
The team’s work has been named a finalist for the Association of Computing Machinery’s Gordon Bell Prize, one of the top honors in high-performance computing.
The milestone marks both a scientific and computational breakthrough. It’s the first time that light-matter interactions in quantum materials have been modeled at this scale and level of detail. The effort also demonstrates how physics-based simulations can evolve alongside AI workloads to drive advances in speed, scale and accuracy.
Shining a Light on Quantum Materials
Quantum materials display unusual behaviors that arise from the laws of quantum mechanics. The movement of electrons can produce properties like superconductivity and magnetism, which could transform future computing, energy storage and sensing technologies. Light can also influence how these materials behave, giving researchers a way to control or enhance their properties.
Capturing how light interacts with quantum materials in a single simulation, however, poses a major challenge. The problem spans multiple length and time scales—from the atomic level to the device level—and involves many different kinds of physics operating simultaneously, a process known as multiscale simulation.
To address this challenge, the USC team created the Multiscale Light-Matter Dynamics (MLMD) software framework. It combines methods to model how light interacts with matter and uses AI to track the movement of atoms. Together, these tools connect small-scale atomic motion to larger material structures.
The software uses a unique divide-conquer-recombine strategy, breaking down the scientific problem into smaller pieces, assigning each to the most efficient computing resource (such as GPUs or CPUs), and then merging the results to form a complete simulation.
To extend the simulations further, the USC team collaborated with Harvard researchers to integrate an AI foundation model named Allegro-FM. Trained on a broad range of materials, Allegro-FM predicts atomic behavior with quantum-level accuracy while scaling to systems of more than a trillion atoms, enabling large-scale simulations across nearly the entire periodic table.
A Quantum Leap in Simulation
Aurora is equipped with 10,624 nodes and 63,744 GPUs, making it one of the largest and most powerful systems ever built. Working closely with experts from the ALCF and Intel, the USC-led team prepared their software for Aurora’s unique architecture, resolving scaling challenges and ensuring the system could handle extremely large workloads efficiently.
Running on the full Aurora system, their framework delivered up to 3,780x faster time-to-solution than previous methods. The team’s simulations aim to reveal how light can trigger structural changes in ferroelectric topological materials —compounds that can switch their internal electric structure and conduct electricity in unusually stable ways. These combined properties make them promising candidates for future low-power, high-speed devices.
The team’s largest simulations focused on lead titanate (PbTiO₃), modeling a system of up to 1.2 trillion atoms. By simulating how light can switch ferroelectric topological materials, the team demonstrated a path toward petahertz electronics, which operate millions of times faster than today’s transistors while consuming far less energy. These materials could ultimately form the foundation for a new class of ultrafast, low-power devices known as topotronics.
The team’s success on Aurora shows how quantum physics, supercomputing and AI are converging to solve some of the most complex problems in science. For the first time, the researchers have simulated the interplay of light, electrons, and atoms at exascale, providing insights that could accelerate the design of next-generation materials, devices, and experiments.
The team’s study, “Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices,” was authored by Taufeq Mohammed Razakh, Rajiv Kalia, Priya Vashishta, Ken-ichi Nomura and Aiichiro Nakano from USC; Thomas Linker from DOE’s SLAC National Accelerator Laboratory; Ye Luo from Argonne; Nariman Piroozan, John Pennycook and Nalini Kumar from Intel; Albert Musaelian, Anders Johansson and Boris Kozinsky from Harvard; Fuyuki Shimojo from Kumamoto University; and Shinnosuke Hattori from Sony Group Corporation.
Cover Image Credit: Joseph Insley, ALCF Visualization and Data Analytics Team
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