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Media Contacts
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
When Andrew Sutton arrived at ORNL in late 2020, he knew the move would be significant in more ways than just a change in location.
A force within the supercomputing community, Jack Dongarra developed software packages that became standard in the industry, allowing high-performance computers to become increasingly more powerful in recent decades.
Oak Ridge National Laboratory scientist Amy Elliott is one of 120 women featured in a new exhibit, IfThenSheCan, at the Smithsonian to commemorate Women's History Month. A life-size 3D printed statue of Elliott, a manufacturing scientist, is on display in the Smithsonian Castle in Washington, D.C.
ORNL, TVA and TNECD were recognized by the Federal Laboratory Consortium for their impactful partnership that resulted in a record $2.3 billion investment by Ultium Cells, a General Motors and LG Energy Solution joint venture, to build a battery cell manufacturing plant in Spring Hill, Tennessee.
Oak Ridge National Laboratory researchers recently used large-scale additive manufacturing with metal to produce a full-strength steel component for a wind turbine, proving the technique as a viable alternative to
A novel method to 3D print components for nuclear reactors, developed by the Department of Energy’s Oak Ridge National Laboratory, has been licensed by Ultra Safe Nuclear Corporation.
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.