Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
OAK RIDGE, Tenn., March 11, 2019—An international collaboration including scientists at the Department of Energy’s Oak Ridge National Laboratory solved a 50-year-old puzzle that explains why beta decays of atomic nuclei
OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.
Gleaning valuable data from social platforms such as Twitter—particularly to map out critical location information during emergencies— has become more effective and efficient thanks to Oak Ridge National Laboratory.
A University of South Carolina research team is investigating the oxygen reduction performance of energy conversion materials called perovskites by using neutron diffraction at Oak Ridge National Laboratory’s Spallation Neutron Source.
Scientists at Oak Ridge National Laboratory and Hypres, a digital superconductor company, have tested a novel cryogenic, or low-temperature, memory cell circuit design that may boost memory storage while using less energy in future exascale and quantum computing applications.
Scientists at the Department of Energy’s Oak Ridge National Laboratory have created a recipe for a renewable 3D printing feedstock that could spur a profitable new use for an intractable biorefinery byproduct: lignin.
The US Department of Energy’s Oak Ridge National Laboratory is once again officially home to the fastest supercomputer in the world, according to the TOP500 List, a semiannual ranking of the world’s fastest computing systems.
The U.S. Department of Energy’s Oak Ridge National Laboratory today unveiled Summit as the world’s most powerful and smartest scientific supercomputer.
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the