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Researchers from ORNL’s Vehicle and Autonomy Research Group created a control strategy for a hybrid electric bus that demonstrated up to 30% energy savings. Credit: University of California, Riverside

Oak Ridge National Laboratory researchers developed and demonstrated algorithm-based controls for a hybrid electric bus that yielded up to 30% energy savings compared with existing controls.

ORNL researchers are examining ways to increase the amount of carbon sequestered in soils by crops such as switchgrass. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

Nearly a billion acres of land in the United States is dedicated to agriculture, producing more than a trillion dollars of food products to feed the country and the world. Those same agricultural processes, however, also produced an estimated 700 million metric tons of carbon dioxide equivalent in 2018, according to the U.S. Department of Agriculture.

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As the United States transitions to clean energy, the country has an ambitious goal: cut carbon dioxide emissions in half by the year 2030, if not before. One of the solutions to help meet this challenge is found at ORNL as part of the Better Plants Program.

David Sholl is director of the new ORNL Transformational Decarbonization Initiative, working to elevate the lab’s prominence in decarbonization science and technology. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy.

David Sholl has come to the U.S. Department of Energy’s Oak Ridge National Laboratory with a wealth of scientific expertise and a personal mission: hasten the development and deployment of decarbonization solutions for the nation’s energy system.

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.

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.

Oak Ridge National Laboratory’s MENNDL AI software system can design thousands of neural networks in a matter of hours. One example uses a driving simulator to evaluate a network’s ability to perceive objects under various lighting conditions. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.

Kashif Nawaz, researcher and group leader for multifunctional equipment integration in buildings technologies, is developing a platform for the direct air capture of carbon dioxide that can be retrofitted to existing rooftop heating, ventilation and air conditioning units.  Credit: ORNL/U.S. Dept. of Energy

When Kashif Nawaz looks at a satellite map of the U.S., he sees millions of buildings that could hold a potential solution for the capture of carbon dioxide, a plentiful gas that can be harmful when excessive amounts are released into the atmosphere, raising the Earth’s temperature.

An X-ray CT image of a 3D-printed metal turbine blade was reconstructed using ORNL’s neural network and advanced algorithms. Credit: Amir Ziabari/ORNL, U.S. Dept. of Energy

Algorithms developed at Oak Ridge National Laboratory can greatly enhance X-ray computed tomography images of 3D-printed metal parts, resulting in more accurate, faster scans.