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Media Contacts
![The protease protein is both shaped like a heart and functions as one, allowing the virus replicate and spread. Inhibiting the protease would block virus reproduction. Credit: Andrey Kovalevsky/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-06/protease_dimer_3_1.png?h=aa51a450&itok=sJY7AB8d)
A team of researchers has performed the first room-temperature X-ray measurements on the SARS-CoV-2 main protease — the enzyme that enables the virus to reproduce.
![Computing – Mining for COVID-19 connections](/sites/default/files/styles/list_page_thumbnail/public/2020-05/pubmedconnections-covid-19-2_0.png?h=3dbd9eac&itok=NPdQ3tCD)
Scientists have tapped the immense power of the Summit supercomputer at Oak Ridge National Laboratory to comb through millions of medical journal articles to identify potential vaccines, drugs and effective measures that could suppress or stop the
![Catherine Schuman, top right, spoke to Copper Ridge Elementary School fifth graders about her job as an ORNL computer scientist as part of the lab’s STEM outreach during the COVID-19 pandemic. Credit: Abby Bower/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-05/schuman_zoom_bb%5B1%5D_0.png?h=4a2c0135&itok=rq5SvE9T)
With Tennessee schools online for the rest of the school year, researchers at ORNL are making remote learning more engaging by “Zooming” into virtual classrooms to tell students about their science and their work at a national laboratory.
![Coronavirus graphic](/sites/default/files/styles/list_page_thumbnail/public/2020-04/covid19_jh_0.png?h=d1cb525d&itok=PyngFUZw)
In the race to identify solutions to the COVID-19 pandemic, researchers at the Department of Energy’s Oak Ridge National Laboratory are joining the fight by applying expertise in computational science, advanced manufacturing, data science and neutron science.
![The agreement builds upon years of collaboration, including a 2016 effort using modeling tools developed at ORNL to predict the first six months of operations of TVA’s Watts Bar Unit 2 nuclear power plant. Credit: Andrew Godfrey/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-02/wb2_xenon_1.png?h=19940d61&itok=Da4pDLde)
OAK RIDGE, Tenn., Feb. 19, 2020 — The U.S. Department of Energy’s Oak Ridge National Laboratory and the Tennessee Valley Authority have signed a memorandum of understanding to evaluate a new generation of flexible, cost-effective advanced nuclear reactors.
![The image visualizes how the team’s multitask convolutional neural network classifies primary cancer sites. Image credit: Hong-Jun Yoon/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2020-02/shot_0.png?h=49ab6177&itok=IXL5Ingy)
As the second-leading cause of death in the United States, cancer is a public health crisis that afflicts nearly one in two people during their lifetime.
![This simulation of a fusion plasma calculation result shows the interaction of two counter-streaming beams of super-heated gas. Credit: David L. Green/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-02/Fusion_plasma_simulation.jpg?h=d0852d1e&itok=CDWgjLPL)
The prospect of simulating a fusion plasma is a step closer to reality thanks to a new computational tool developed by scientists in fusion physics, computer science and mathematics at ORNL.
![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