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Media Contacts
![Julie Smith Julie Smith](/sites/default/files/styles/list_page_thumbnail/public/julie_smith_bb.png?itok=Z9DoY2ss)
It may take a village to raise a child, according to the old proverb, but it takes an entire team of highly trained scientists and engineers to install and operate a state-of-the-art, exceptionally complex ion microprobe. Just ask Julie Smith, a nuclear security scientist at the Depa...
![Default image of ORNL entry sign](/sites/default/files/styles/list_page_thumbnail/public/2023-09/default-thumbnail.jpg?h=553c93cc&itok=N_Kd1DVR)
James Peery, who led critical national security programs at Sandia National Laboratories and held multiple leadership positions at Los Alamos National Laboratory before arriving at the Department of Energy’s Oak Ridge National Laboratory last year, has been named a...
![From left, Andrew Lupini and Juan Carlos Idrobo use ORNL’s new monochromated, aberration-corrected scanning transmission electron microscope, a Nion HERMES to take the temperatures of materials at the nanoscale. Image credit: Oak Ridge National Laboratory From left, Andrew Lupini and Juan Carlos Idrobo use ORNL’s new monochromated, aberration-corrected scanning transmission electron microscope, a Nion HERMES to take the temperatures of materials at the nanoscale. Image credit: Oak Ridge National Laboratory](/sites/default/files/styles/list_page_thumbnail/public/news/images/2018-P00413.jpg?itok=UKejk7r2)
A scientific team led by the Department of Energy’s Oak Ridge National Laboratory has found a new way to take the local temperature of a material from an area about a billionth of a meter wide, or approximately 100,000 times thinner than a human hair. This discove...
![Germina Ilas (left) and Ian Gauld review spent fuel data entries in the SFCOMPO 2.0 database. Germina Ilas (left) and Ian Gauld review spent fuel data entries in the SFCOMPO 2.0 database.](/sites/default/files/styles/list_page_thumbnail/public/news/images/2018-P00005_r3_0.jpg?itok=FrGhhOuK)
![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
![ORNL researcher Miaofang Chi refines her microscopy techniques toward understanding how and why materials have certain properties. ORNL researcher Miaofang Chi refines her microscopy techniques toward understanding how and why materials have certain properties.](/sites/default/files/styles/list_page_thumbnail/public/M_Chi_casual_0.png?itok=uvQT5OzH)
Material surfaces and interfaces may appear flat and void of texture to the naked eye, but a view from the nanoscale reveals an intricate tapestry of atomic patterns that control the reactions between the material and its environment. Electron microscopy allows researchers to probe...
![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 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](/sites/default/files/styles/list_page_thumbnail/public/01%201%20-%20Impacts%20r1.jpg?itok=D1FzgK0y)
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...
![ORNL’s Xiahan Sang unambiguously resolved the atomic structure of MXene, a 2D material promising for energy storage, catalysis and electronic conductivity. Image credit: Oak Ridge National Laboratory, U.S. Dept. of Energy; photographer Carlos Jones ORNL’s Xiahan Sang unambiguously resolved the atomic structure of MXene, a 2D material promising for energy storage, catalysis and electronic conductivity. Image credit: Oak Ridge National Laboratory, U.S. Dept. of Energy; photographer Carlos Jones](/sites/default/files/styles/list_page_thumbnail/public/Sang_2016-P07680_0.jpg?itok=w0e5eR_U)
Researchers have long sought electrically conductive materials for economical energy-storage devices. Two-dimensional (2D) ceramics called MXenes are contenders. Unlike most 2D ceramics, MXenes have inherently good conductivity because they are molecular sheets made from the carbides ...
![The Transforming Additive Manufacturing through Exascale Simulation project (ExaAM) is building a new multi-physics modeling and simulation platform for 3D printing of metals The Transforming Additive Manufacturing through Exascale Simulation project (ExaAM) is building a new multi-physics modeling and simulation platform for 3D printing of metals](/sites/default/files/styles/list_page_thumbnail/public/ECP%20release%20graphic%202_0.jpg?itok=JzmmCpGX)
Oak Ridge National Laboratory experts are playing leading roles in the recently established Department of Energy’s (DOE’s) Exascale Computing Project (ECP), a multi-lab initiative responsible for developing the strategy, aligning the resources, and conducting the R&D necessary to achieve the nation’s imperative of delivering exascale computing by 2021.
![Default image of ORNL entry sign](/sites/default/files/styles/list_page_thumbnail/public/2023-09/default-thumbnail.jpg?h=553c93cc&itok=N_Kd1DVR)
The Department of Energy’s Oak Ridge National Laboratory has received funding from DOE’s Exascale Computing Project (ECP) to develop applications for future exascale systems that will be 50 to 100 times more powerful than today’s fastest supercomputers.