Researchers from ORNL and international collaborators discovered a simpler, lower-pressure method to produce R8 silicon, a rare form of silicon with promising applications in energy storage and electronics. By compressing amorphous (glassy) silicon instead of crystalline silicon, the team created R8 more efficiently, with neutron scattering, X-ray diffraction, and computer modeling confirming the process and suggesting it could also work for other materials like germanium.
Oak Ridge National Laboratory’s OLCF provided a Lawrence Berkeley National Laboratory scientist with remote access to an IBM quantum computer through its Quantum Computer User Program (QCUP) to simulate hadronization, a key process in particle physics. The project reproduced results from previous classical simulations using a simplified quantum mechanical model and serves as a step toward using quantum computers for larger quantum chromodynamics calculations.
Tennessee Tech students partnered with ORNL to develop a tool that automatically extracts manufacturing equipment data from photos, reducing manual data entry. The project gives students real-world industry experience while helping ORNL improve its manufacturing software.
ORNL developed JACC, an open-source framework that enables Julia applications to run efficiently on CPUs and GPUs from multiple hardware vendors using a single codebase. The software improves performance portability for high-performance computing systems and reduces the need for architecture-specific code.
ORNL had a major presence at the 2026 AI+ Expo for National Competitiveness with multiple ORNL researchers presenting and demoing their latest work. Hosted by the Special Competitive Studies Project, the event brought together leaders from technology, academia, industry and government to discuss the rapidly evolving role of artificial intelligence in advancing U.S. innovation, energy resilience, competitiveness and national security.
Robert Stewart turned a childhood fascination with maps and computers into a career at ORNL developing geospatial and AI tools for environmental cleanup and human security. His work has influenced nuclear remediation practices and supported efforts credited with saving lives. Today, he focuses on mentoring future scientists and advancing data-driven research at ORNL.
DOE's ROVI initiative, led in part by ORNL, successfully demonstrated the ROVI DataHub, a secure platform that unifies energy storage data across national labs and field projects to accelerate validation and deployment of long-duration energy storage technologies. The system supports real-time analytics, AI workflows and standardized reporting for next-generation battery technologies.
David Page, head of the Geographic Data Science Section at Oak Ridge National Laboratory, leads with a focus on judgment, collaboration and empowering teams. Drawing on experience in academia, entrepreneurship and national security science, he has helped advance faster, scalable geospatial analysis while building resilient, forward-looking research teams.
An ORNL AI model can accurately predict river temperatures across the U.S., even in waterways without sensors. The system achieves about a 1.1°C median error and can estimate temperatures for all river reaches nationwide. Researchers say it could improve power plant cooling, energy reliability and environmental management.
A region rich in natural resources took centerstage recently at Oak Ridge National Laboratory’s third Appalachian Carbon Forum in Morgantown, West Virginia, where leaders in nuclear energy, critical materials and infrastructure exchanged ideas.
Researchers at ORNL used a quantum computer to simulate complex hadron collisions beyond classical capabilities. The work shows quantum computing’s potential to model particle interactions despite current limitations.
Scientists at Oak Ridge National Laboratory are developing AI-enabled pixel detectors that can analyze particle-collision data directly at the source. The approach could help particle-physics experiments identify and capture the most important signals from the enormous amounts of data modern accelerators produce, helping scientists make faster, more informed discoveries from some of the world’s most complex experiments.
