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This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography.  Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.

An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy

An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.

Deborah Frincke, one of the nation’s preeminent computer scientists and cybersecurity experts, serves as associate laboratory director of ORNL’s National Security Science Directorate. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Deborah Frincke, one of the nation’s preeminent computer scientists and cybersecurity experts, serves as associate laboratory director of ORNL’s National Security Science Directorate. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

ORNL’s Sergei Kalinin and Rama Vasudevan (foreground) use scanning probe microscopy to study bulk ferroelectricity and surface electrochemistry -- and generate a lot of data. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.

Verónica Melesse Vergara speaks with third and fourth graders at East Side Intermediate School in Brownsville. Credit: ORNL, U.S. Dept. of Energy

Twenty-seven ORNL researchers Zoomed into 11 middle schools across Tennessee during the annual Engineers Week in February. East Tennessee schools throughout Oak Ridge and Roane, Sevier, Blount and Loudon counties participated, with three West Tennessee schools joining in.

Small, 3D-printed neutron collimators, designed by ORNL’s Jamie Molaison, yield reduced costs and manufacturing times and could enable new types of experiments. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

The ExOne Company, the global leader in industrial sand and metal 3D printers using binder jetting technology, announced it has reached a commercial license agreement with Oak Ridge National Laboratory to 3D print parts in aluminum-infiltrated boron carbide.

Blue sky above ORNL campus.

ORNL and three partnering institutions have received $4.2 million over three years to apply artificial intelligence to the advancement of complex systems in which human decision making could be enhanced via technology.

Coronavirus graphic

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.

Scientists created a novel polymer that is as effective as natural proteins in transporting protons through a membrane. Credit: ORNL/Jill Hemman

Biological membranes, such as the “walls” of most types of living cells, primarily consist of a double layer of lipids, or “lipid bilayer,” that forms the structure, and a variety of embedded and attached proteins with highly specialized functions, including proteins that rapidly and selectively transport ions and molecules in and out of the cell.

The image visualizes how the team’s multitask convolutional neural network classifies primary cancer sites. Image credit: Hong-Jun Yoon/ORNL

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.