ORNL is home to the world's fastest exascale supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations.
Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
Three researchers at ORNL have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
Laboratory Director Thomas Zacharia presented five Director’s Awards during Saturday night's annual Awards Night event hosted by UT-Battelle, which manages ORNL for the Department of Energy.
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
ORNL, TVA and TNECD were recognized by the Federal Laboratory Consortium for their impactful partnership that resulted in a record $2.3 billion investment by Ultium Cells, a General Motors and LG Energy Solution joint venture, to build a battery cell manufacturing plant in Spring Hill, Tennessee.
To minimize potential damage from underground oil and gas leaks, Oak Ridge National Laboratory is co-developing a quantum sensing system to detect pipeline leaks more quickly.
Using complementary computing calculations and neutron scattering techniques, researchers from the Department of Energy’s Oak Ridge and Lawrence Berkeley national laboratories and the University of California, Berkeley, discovered the existence of an elusive type of spin dynamics in a quantum mechanical system.
Algorithms developed at Oak Ridge National Laboratory can greatly enhance X-ray computed tomography images of 3D-printed metal parts, resulting in more accurate, faster scans.