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Media Contacts
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.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.
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
In the 1960s, Oak Ridge National Laboratory's four-year Molten Salt Reactor Experiment tested the viability of liquid fuel reactors for commercial power generation. Results from that historic experiment recently became the basis for the first-ever molten salt reactor benchmark.
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.
A new method developed at Oak Ridge National Laboratory improves the energy efficiency of a desalination process known as solar-thermal evaporation.
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
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.
Oak Ridge National Laboratory physicists studying quantum sensing, which could impact a wide range of potential applications from airport security scanning to gravitational wave measurements, have outlined in ACS Photonics the dramatic advances in the field.
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