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.
Ten scientists from the Department of Energy’s Oak Ridge National Laboratory are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
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.
Researchers at ORNL designed a novel polymer to bind and strengthen silica sand for binder jet additive manufacturing, a 3D-printing method used by industries for prototyping and part production.
A team led by ORNL and the University of Michigan have discovered that certain bacteria can steal an essential compound from other microbes to break down methane and toxic methylmercury in the environment.
Anyone familiar with ORNL knows it’s a hub for world-class science. The nearly 33,000-acre space surrounding the lab is less known, but also unique.
Moving to landlocked Tennessee isn’t an obvious choice for most scientists with new doctorate degrees in coastal oceanography.
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
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.
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.