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(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.

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

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.

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Scientists from the Critical Materials Institute used the Titan supercomputer and Eos computing cluster at ORNL to analyze designer molecules that could increase the yield of rare earth elements found in bastnaesite, an important mineral 

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.

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

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...

This isotropic, neodymium-iron-boron bonded permanent magnet was 3D-printed at DOE’s Manufacturing Demonstration Facility at Oak Ridge National Laboratory.

Researchers at the Department of Energy’s Oak Ridge National Laboratory have demonstrated that permanent magnets produced by additive manufacturing can outperform bonded magnets made using traditional techniques while conserving critical materials. Scientists fabric...

ORNL Director Thom Mason (left) and Thomas Roberts of Oddello Industries LLC sign a research and development agreement.

A process developed at Oak Ridge National Laboratory for large-scale recovery of rare earth magnets from used computer hard drives will undergo industrial testing under a new agreement between Oddello Industries LLC and ORNL, as part of the Department of Energy’s Crit...

ORNL researchers are developing an idealized collector molecule that has a shape complementary to the surface atomic structure of xenotime, a rare earth yttrium-rich phosphate mineral.

Ensuring a reliable supply of rare earth elements, including four key lanthanides and yttrium, is a major goal of the Critical Materials Institute (https://cmi.ameslab.gov) as these elements are essential to many clean-energy technologies. These include energy-efficient lighting, ...

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A new technology developed by the U.S. Department of Energy’s Critical Materials Institute that aids in the recycling, recovery and extraction of rare earth minerals has been licensed to U.S. Rare Earths, Inc.

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An alloy discovered at Oak Ridge National Laboratory holds great promise for permanent magnets as the material retains its magnetic properties at higher temperatures yet contains no rare-earth elements. This finding is significant because while rare-earth-based magnets are critical to alternative ...