Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Deborah Frincke, one of the nation’s preeminent computer scientists and cybersecurity experts, serves as associate laboratory director of ORNL’s National Security Science Directorate. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.
Thirty-two Oak Ridge National Laboratory employees were named among teams recognized by former DOE Secretary Dan Brouillette with Secretary’s Honor Awards as he completed his term. Four teams received new awards that reflect DOE responses to the coronavirus pandemic.
As Hurricane Dorian raged through the Bahamas, researchers at Oak Ridge National Laboratory worked around the clock to aid recovery efforts for one of the Caribbean’s worst storms ever.
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.
Jeff Johnson, nonproliferation research and development integration manager for ORNL’s National Security Sciences Directorate, has been honored by the American Nuclear Society
Six ORNL scientists have been elected as fellows to the American Association for the Advancement of Science, or AAAS.
ORNL has added 10 virtual tours to its campus map, each with multiple views to show floor plans, rotating dollhouse views and 360-degree navigation. As a user travels through a map, pop-out informational windows deliver facts, videos, graphics and links to other related content.
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.