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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

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...

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...

An ORNL-led team conducts moisture durability simulations on construction materials, which will add real-world data to a new system developed to help builders and home designers make informed decisions. Credit: Rachel Brooks/Oak Ridge National Laboratory,

A new system being developed at Oak Ridge National Laboratory will help builders and home designers select the best construction materials for long-term moisture durability. “It has become challenging to make informed decisions because of modern building code requirements and new ...

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

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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.