The Department of Energy’s Oak Ridge National Laboratory (ORNL) has launched a major four-year research collaboration aimed at changing the way scientists understand nonequilibrium quantum materials.
The initiative brings together national laboratories and academic partners to harness the power of high-performance computing (HPC) and exascale supercomputers to investigate quantum systems far from equilibrium.
The program, known as Controlled Numerics for Emergent Transients in Nonequilibrium Quantum Matter (CONNEQT), is designed to overcome long-standing barriers in modeling and predicting the dynamic behavior of quantum materials under real-world conditions.
Importance of non-equilibrium quantum materials
In real-world environments, materials are rarely stationary. They are constantly exposed to light, heat, electrical current, magnetic fields, or energy currents, all of which disturb their equilibrium state.
For quantum materials, these perturbations can dramatically change electronic and magnetic behavior, sometimes revealing properties that remain hidden when the system is stable.
Understanding nonequilibrium quantum materials is therefore essential for advancing technologies such as quantum computing, microelectronics, sensing, and information processing.
By deliberately disrupting the balance of materials, scientists may be able to engineer new quantum states and control unusual phenomena on demand.
National cooperation with global ambitions
ORNL is leading the CONNEQT effort with researchers at Los Alamos National Laboratory, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory, and the University of Tennessee, Knoxville.
The team is working together to build an interdisciplinary framework that combines physics, applied mathematics, and computer science.
This collaboration aims to address important challenges. Although experimental tools are rapidly advancing, theoretical models and simulations still struggle to describe nonequilibrium quantum behavior over realistic time and length scales.
Bridging that gap is essential to turning laboratory discoveries into practical technologies.
Exascale computing as a science engine
Central to this project is the use of leader-class supercomputers, including ORNL’s Frontier, the world’s first system to cross the exascale threshold.
These machines can perform more than a billion calculations per second, enabling simulations that were previously impossible.
CONNEQT researchers harness this computational power to model strongly interacting quantum systems, such as unconventional superconductors and quantum spin liquids.
These materials exhibit complex many-body effects that require large amounts of computational resources to simulate accurately, especially when far from equilibrium.
Three pillars of research
Over the next four years, the team will pursue three core goals. First, we develop a controlled and unbiased computational framework to study the interactions of electrons subjected to external forces.
Second, we apply advanced mathematics and computer science techniques to accelerate the simulation of highly complex dynamical systems.
Third, we use exascale platforms to reveal how collective electronic interactions give rise to transient patterns and emergent behavior in nonequilibrium quantum materials.
Together, these efforts aim to redefine the state of the art in quantum materials modeling.
Impact on energy and innovation
This research is supported by DOE’s Science Discovery through Advanced Computing program, with funding from the Office of Science’s Division of Advanced Scientific Computing Research and Basic Energy Sciences.
This also aligns with DOE’s Genesis Mission, which aims to build the world’s most robust scientific ecosystem for discovery and innovation.
By combining AI-enabled exascale computing with cutting-edge physics, the CONNEQT collaboration has the potential to accelerate breakthroughs in energy-related technologies, strengthen national competitiveness, and break new ground in nonequilibrium quantum materials research.
Source link
