A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.
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.
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.
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.
To better understand the spread of SARS-CoV-2, the virus that causes COVID-19, Oak Ridge National Laboratory researchers have harnessed the power of supercomputers to accurately model the spike protein that binds the novel coronavirus to a human cell receptor.
Since the 1930s, scientists have been using particle accelerators to gain insights into the structure of matter and the laws of physics that govern our world.
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.
There are more than 17 million veterans in the United States, and approximately half rely on the Department of Veterans Affairs for their healthcare.
Oak Ridge National Laboratory scientists have discovered a cost-effective way to significantly improve the mechanical performance of common polymer nanocomposite materials.