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Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions. Credit: Getty Images

Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions.

Oak Ridge National Laboratory researchers quantified human behaviors during the early days of COVID-19, which could be useful for disaster response or city planning. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

Researchers at Oak Ridge National Laboratory have empirically quantified the shifts in routine daytime activities, such as getting a morning coffee or takeaway dinner, following safer at home orders during the early days of the COVID-19 pandemic.

With seismic and acoustic data recorded by remote sensors near ORNL’s High Flux Isotope Reactor, researchers could predict whether the reactor was on or off with 98% accuracy. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.

Oak Ridge National Laboratory researchers built an Earth-to-space communications system to work with private and government partners with the goal of directly connecting data downlinks to high performance computing. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory is debuting a small satellite ground station that uses high-performance computing to support automated detection of changes to Earth’s landscape.

Researchers at Oak Ridge National Laboratory demonstrated center-of-mass scanning transmission electron microscopy to observe lithium along with heavier elements in battery materials at atomic resolution. Credit: Chad Malone/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory researchers demonstrated an electron microscopy technique for imaging lithium in energy storage materials, such as lithium ion batteries, at the atomic scale.

ORNL researchers worked with partners at the Colorado School of Mines and Baylor University to develop a new process optimization and control method for a closed-circuit reverse osmosis desalination system. The work is intended to support fully automated, decentralized water treatment plants. Credit: Andrew Sproles/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory scientists worked with the Colorado School of Mines and Baylor University to develop and test control methods for autonomous water treatment plants that use less energy and generate less waste.

ORNL’s Eva Zarkadoula seeks piezoelectric materials for sensors that can withstand irradiation, which causes cascading collisions that displace atoms and produces defects. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

To advance sensor technologies, Oak Ridge National Laboratory researchers studied piezoelectric materials, which convert mechanical stress into electrical energy, to see how they could handle bombardment with energetic neutrons.

ORNL researchers observed that atomic vibrations in a twisted crystal result in winding energetic waves that govern heat transport, which may help new materials better manage heat. Credit: Jill Hemman/ORNL, U.S. Dept. of Energy

A discovery by Oak Ridge National Laboratory researchers may aid the design of materials that better manage heat.

Researchers used an atomic force microscope to test how easily particles of the novel coronavirus cling to certain surfaces, a property known as adhesion energy. Credit: Ali Passian/ORNL, U.S. Dept. of Energy

A study by Department of Energy researchers detailed a potential method to detect the novel coronavirus

An open-source code developed by an ORNL-led team could provide new insights into the everyday operation of the nation’s power grid. Credit: Pixabay

Oak Ridge National Laboratory, University of Tennessee and University of Central Florida researchers released a new high-performance computing code designed to more efficiently examine power systems and identify electrical grid disruptions, such as