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Media Contacts
![Tungsten tiles for fusion](/sites/default/files/styles/list_page_thumbnail/public/2019-07/EBM-tungsten_tiles_ORNL.png?h=0c890573&itok=XgIsl0tA)
Using additive manufacturing, scientists experimenting with tungsten at Oak Ridge National Laboratory hope to unlock new potential of the high-performance heat-transferring material used to protect components from the plasma inside a fusion reactor. Fusion requires hydrogen isotopes to reach millions of degrees.
A team of scientists led by Oak Ridge National Laboratory have discovered the specific gene that controls an important symbiotic relationship between plants and soil fungi, and successfully facilitated the symbiosis in a plant that
![Small modular reactor computer simulation](/sites/default/files/styles/list_page_thumbnail/public/2019-04/Nuclear_simulation_scale-up.jpg?h=5992a83f&itok=A0oscIPL)
In a step toward advancing small modular nuclear reactor designs, scientists at Oak Ridge National Laboratory have run reactor simulations on ORNL supercomputer Summit with greater-than-expected computational efficiency.
![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.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Slide1_0.png?h=c855054e&itok=aNbgxXsc)
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.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/TourassiWH%5B1%5D.png?h=26b5064d&itok=HUC2iYmE)
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.
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).
![X1800-REED-Maritime Risk Symposium 2018 logo-AM V5-01.jpg X1800-REED-Maritime Risk Symposium 2018 logo-AM V5-01.jpg](/sites/default/files/styles/list_page_thumbnail/public/X1800-REED-Maritime%20Risk%20Symposium%202018%20logo-AM%20V5-01.jpg?itok=_AN4HV63)
Thought leaders from across the maritime community came together at Oak Ridge National Laboratory to explore the emerging new energy landscape for the maritime transportation system during the Ninth Annual Maritime Risk Symposium.
![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’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.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
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