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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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Researchers used quantum Monte Carlo calculations to accurately render the structure and electronic properties of germanium selenide, a semiconducting nanomaterial. Credit: Paul Kent/ORNL, U.S. Dept. of Energy

Improving materials for energy storage, generation

A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.

The AI-driven HyperCT platform has three primary points of articulation that can rotate a sample in almost any direction, eliminating the need for human intervention and significantly reducing lengthy experiment times. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy

Automating neutron experiments with AI

Oak Ridge National Laboratory researchers are developing a first-of-its-kind artificial intelligence device for neutron scattering called Hyperspectral Computed Tomography, or HyperCT.