Oak Ridge National Laboratory, University of Tennessee and University of Central Florida researchers released a new high-performance computing code designed to more efficiently examine power systems and identify electrical grid disruptions, such as
An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.
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
At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.
For a researcher who started out in mechanical engineering with a focus on engine combustion, Martin Wissink has learned a lot about neutrons on the job
IDEMIA Identity & Security USA has licensed an advanced optical array developed at Oak Ridge National Laboratory. The portable technology can be used to help identify individuals in challenging outdoor conditions.
Oak Ridge National Laboratory is training next-generation cameras called dynamic vision sensors, or DVS, to interpret live information—a capability that has applications in robotics and could improve autonomous vehicle sensing.
Researchers at Oak Ridge National Laboratory are taking inspiration from neural networks to create computers that mimic the human brain—a quickly growing field known as neuromorphic computing.
Scientists at Oak Ridge National Laboratory studying quantum communications have discovered a more practical way to share secret messages among three parties, which could ultimately lead to better cybersecurity for the electric grid
A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool