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Media Contacts
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 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.
In the quest for advanced vehicles with higher energy efficiency and ultra-low emissions, ORNL researchers are accelerating a research engine that gives scientists and engineers an unprecedented view inside the atomic-level workings of combustion engines in real time.
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.
A multi-institutional team, led by a group of investigators at Oak Ridge National Laboratory, has been studying various SARS-CoV-2 protein targets, including the virus’s main protease. The feat has earned the team a finalist nomination for the Association of Computing Machinery, or ACM, Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research.
Scientists from Oak Ridge National Laboratory used high-performance computing to create protein models that helped reveal how the outer membrane is tethered to the cell membrane in certain bacteria.
There are more than 17 million veterans in the United States, and approximately half rely on the Department of Veterans Affairs for their healthcare.
Radioactive isotopes power some of NASA’s best-known spacecraft. But predicting how radiation emitted from these isotopes might affect nearby materials is tricky
Researchers at ORNL used quantum optics to advance state-of-the-art microscopy and illuminate a path to detecting material properties with greater sensitivity than is possible with traditional tools.