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![After a monolayer MXene is heated, functional groups are removed from both surfaces. Titanium and carbon atoms migrate from one area to both surfaces, creating a pore and forming new structures. Credit: ORNL, USDOE; image by Xiahan Sang and Andy Sproles. After a monolayer MXene is heated, functional groups are removed from both surfaces. Titanium and carbon atoms migrate from one area to both surfaces, creating a pore and forming new structures. Credit: ORNL, USDOE; image by Xiahan Sang and Andy Sproles.](/sites/default/files/styles/list_page_thumbnail/public/news/images/hTiC04_v2.jpg?itok=GeDQD6xS)
Scientists at the Department of Energy’s Oak Ridge National Laboratory induced a two-dimensional material to cannibalize itself for atomic “building blocks” from which stable structures formed. The findings, reported in Nature Communications, provide insights that ...
![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...
![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