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An international team of researchers has discovered the hydrogen atoms in a metal hydride material are much more tightly spaced than had been predicted for decades — a feature that could possibly facilitate superconductivity at or near room temperature and pressure.
Researchers across the scientific spectrum crave data, as it is essential to understanding the natural world and, by extension, accelerating scientific progress.
For nearly three decades, scientists and engineers across the globe have worked on the Square Kilometre Array (SKA), a project focused on designing and building the world’s largest radio telescope. Although the SKA will collect enormous amounts of precise astronomical data in record time, scientific breakthroughs will only be possible with systems able to efficiently process that data.
Illustration of the optimized zeolite catalyst, or NbAlS-1, which enables a highly efficient chemical reaction to create butene, a renewable source of energy, without expending high amounts of energy for the conversion. Credit: Jill Hemman, Oak Ridge National Laboratory/U.S. Dept. of Energy
Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
The U.S. Department of Energy’s Office of Science announced allocations of supercomputer access to 47 science projects for 2020.
Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.
The type of vehicle that will carry people to the Red Planet is shaping up to be “like a two-story house you’re trying to land on another planet.
Processes like manufacturing aircraft parts, analyzing data from doctors’ notes and identifying national security threats may seem unrelated, but at the U.S. Department of Energy’s Oak Ridge National Laboratory, artificial intelligence is improving all of these tasks.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.