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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.

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.

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.

An ORNL-developed graphite foam, which could be used in plasma-facing components in fusion reactors, performed well during testing at the Wendlestein 7-X stellarator in Germany.

Scientists have tested a novel heat-shielding graphite foam, originally created at Oak Ridge National Laboratory, at Germany’s Wendelstein 7-X stellarator with promising results for use in plasma-facing components of fusion reactors.

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.

Default image of ORNL entry sign

If you ask the staff and researchers at the Department of Energy’s Oak Ridge National Laboratory how they were first referred to the lab, you will get an extremely varied list of responses. Some may have come here as student interns, some grew up in the area and knew the lab by ...

Lauren Garrison

The materials inside a fusion reactor must withstand one of the most extreme environments in science, with temperatures in the thousands of degrees Celsius and a constant bombardment of neutron radiation and deuterium and tritium, isotopes of hydrogen, from the volatile plasma at th...

A simulation of runaway electrons in the experimental tokamak at the DIII-D National Fusion Facility at General Atomics shows the particle orbits in the fusion plasma and the synchrotron radiation emission patterns. Credit: Oak Ridge National Laboratory,

Fusion scientists from Oak Ridge National Laboratory are studying the behavior of high-energy electrons when the plasma that generates nuclear fusion energy suddenly cools during a magnetic disruption. Fusion energy is created when hydrogen isotopes are heated to millions of degrees...

A conceptual illustration of proton-proton fusion in which two protons fuse to form a deuteron. Image courtesy of William Detmold.

Nuclear physicists are using the nation’s most powerful supercomputer, Titan, at the Oak Ridge Leadership Computing Facility to study particle interactions important to energy production in the Sun and stars and to propel the search for new physics discoveries Direct calculatio...

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...