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Researchers at the Department of Energy’s Oak Ridge National Laboratory and their technologies have received seven 2022 R&D 100 Awards, plus special recognition for a battery-related green technology product.
A force within the supercomputing community, Jack Dongarra developed software packages that became standard in the industry, allowing high-performance computers to become increasingly more powerful in recent decades.
More than 50 current employees and recent retirees from ORNL received Department of Energy Secretary’s Honor Awards from Secretary Jennifer Granholm in January as part of project teams spanning the national laboratory system. The annual awards recognized 21 teams and three individuals for service and contributions to DOE’s mission and to the benefit of the nation.
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
Six ORNL scientists have been elected as fellows to the American Association for the Advancement of Science, or AAAS.
Oak Ridge National Laboratory scientists have discovered a cost-effective way to significantly improve the mechanical performance of common polymer nanocomposite materials.
Scientists have tapped the immense power of the Summit supercomputer at Oak Ridge National Laboratory to comb through millions of medical journal articles to identify potential vaccines, drugs and effective measures that could suppress or stop the
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
The prospect of simulating a fusion plasma is a step closer to reality thanks to a new computational tool developed by scientists in fusion physics, computer science and mathematics at ORNL.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.