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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.

The EPB Control Center monitors the company’s assets such as substations and buildings.

OAK RIDGE, Tenn., Feb. 12, 2019—A team of researchers from the Department of Energy’s Oak Ridge and Los Alamos National Laboratories has partnered with EPB, a Chattanooga utility and telecommunications company, to demonstrate the effectiveness of metro-scale quantum key distribution (QKD).

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While studying the genes in poplar trees that control callus formation, scientists at Oak Ridge National Laboratory have uncovered genetic networks at the root of tumor formation in several human cancers.

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Oak Ridge National Laboratory scientists studying fuel cells as a potential alternative to internal combustion engines used sophisticated electron microscopy to investigate the benefits of replacing high-cost platinum with a lower cost, carbon-nitrogen-manganese-based catalyst.

Joseph Lukens, Raphael Pooser, and Nick Peters (from left) of ORNL’s Quantum Information Science Group developed and tested a new interferometer made from highly nonlinear fiber in pursuit of improved sensitivity at the quantum scale. Credit: Carlos Jones

By analyzing a pattern formed by the intersection of two beams of light, researchers can capture elusive details regarding the behavior of mysterious phenomena such as gravitational waves. Creating and precisely measuring these interference patterns would not be possible without instruments called interferometers.

Rose Ruther and Jagjit Nanda have been collaborating to develop a membrane for a low-cost redox flow battery for grid-scale energy storage.

Oak Ridge National Laboratory scientists have developed a crucial component for a new kind of low-cost stationary battery system utilizing common materials and designed for grid-scale electricity storage. Large, economical electricity storage systems can benefit the nation’s grid ...

Graphical representation of a deuteron, the bound state of a proton (red) and a neutron (blue). Credit: Andy Sproles/Oak Ridge National Laboratory, U.S. Dept. of Energy.

Scientists at the Department of Energy’s Oak Ridge National Laboratory are the first to successfully simulate an atomic nucleus using a quantum computer. The results, published in Physical Review Letters, demonstrate the ability of quantum systems to compute nuclear ph...

Materials—Polymer-theory-problem

Scientists at Oak Ridge National Laboratory have conducted a series of breakthrough experimental and computational studies that cast doubt on a 40-year-old theory describing how polymers in plastic materials behave during processing.

Oak Ridge National Laboratory researcher Halil Tekinalp combines silanes and polylactic acid to create supertough renewable plastic.

A novel method developed at Oak Ridge National Laboratory creates supertough renewable plastic with improved manufacturability. Working with polylactic acid, a biobased plastic often used in packaging, textiles, biomedical implants and 3D printing, the research team added tiny amo...

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