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How artificial intelligence can transform US energy infrastructure
Surprisingly, changing isotope masses of molybdenum in a single layer of semiconductor molybdenum disulfide was found to shift the color of light emitted when the layer was illuminated. The study revealed the potential of isotope engineering to design new technologies in 2D materials. Credit: Chris Rouleau/ORNL, U.S. Dept. of Energy
Tweaking isotopes sheds light on semiconductors
The Linac Coherent Light Source at DOE’s SLAC National Accelerator Laboratory in California reveals the structural dynamics of atoms and molecules through X-ray snapshots at ultrafast timescales. Pictured here is the LCLS-II tunnel. Credit: Jim Gensheimer/SLAC National Accelerator Laboratory
ORNL, SLAC team up for biology projects

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The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

Updated exascale system for Earth simulations delivers twice the speed

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

Ultra Safe Nuclear Corporation has licensed a novel method to 3D print highly resistant components for use in nuclear reactor designs. USNC Executive Vice President Kurt Terrani, formerly of ORNL, said the novel method will allow the company to make parts with desired complex shapes more efficiently. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Ultra Safe Nuclear licenses ORNL method to 3D print advanced reactor components

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.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

Scientists use Summit supercomputer, deep learning to predict protein functions at genome scale

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.