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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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Molecular dynamics simulations of the Fs-peptide revealed the presence of at least eight distinct intermediate stages during the process of protein folding. The image depicts a fully folded helix (1), various transitional forms (2–8), and one misfolded state (9). By studying these protein folding pathways, scientists hope to identify underlying factors that affect human health.

Modified deep-learning algorithms unveil features of shape-shifting proteins

Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.

(From left) ORNL Associate Laboratory Director for Computing and Computational Sciences Jeff Nichols; ORNL Health Data Sciences Institute Director Gina Tourassi; DOE Deputy Under Secretary for Science Thomas Cubbage; ORNL Task Lead for Biostatistics Blair Christian; and ORNL Research Scientist Ioana Danciu were invited to the White House to showcase an ORNL-developed digital tool aimed at better matching cancer patients with clinical trials.

New ORNL AI tool revolutionizes process for matching cancer patients with clinical trials

OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.

ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La

Commuting—Oh, the places you’ll go

Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

ORNL researchers use Titan to accelerate design, training of deep learning networks

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the

As hurricanes formed in the Gulf Coast, ORNL activated a computing technique to quickly gather building structure data from Texas’ coastal counties. Credit: Mark Tuttle/Oak Ridge National Laboratory, U.S. Dept. of Energy

Datasets—Supporting hurricane damage assessments

Geospatial scientists at Oak Ridge National Laboratory have developed a novel method to quickly gather building structure datasets that support emergency response teams assessing properties damaged by Hurricanes Harvey and Irma. By coupling deep learning with high-performance comp...