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Media Contacts
A study led by researchers at ORNL used the nation’s fastest supercomputer to close in on the answer to a central question of modern physics that could help conduct development of the next generation of energy technologies.
A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.
A world-leading researcher in solid electrolytes and sophisticated electron microscopy methods received Oak Ridge National Laboratory’s top science honor today for her work in developing new materials for batteries. The announcement was made during a livestreamed Director’s Awards event hosted by ORNL Director Thomas Zacharia.
The U.S. Department of Energy’s Office of Science announced allocations of supercomputer access to 51 high-impact computational science projects for 2022 through its Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program.
The daily traffic congestion along the streets and interstate lanes of Chattanooga could be headed the way of the horse and buggy with help from ORNL researchers.
Improved data, models and analyses from ORNL scientists and many other researchers in the latest global climate assessment report provide new levels of certainty about what the future holds for the planet
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.