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UnifyFS team wins IPDPS award for open-source software

A research team from the Department of Energy’s Oak Ridge and Lawrence Livermore national laboratories won the first Best Open-Source Contribution Award for its paper at the 37th IEEE International Parallel and Distributed Processing Symposium.

Researchers captured atomic-level insights on the rare-earth mineral monazite to inform future design of flotation collector molecules, illustrated above, that can aid in the recovery of critical materials. Credit: Chad Malone/ORNL, U.S. Dept. of Energy

Critical Materials Institute researchers at Oak Ridge National Laboratory and Arizona State University studied the mineral monazite, an important source of rare-earth elements, to enhance methods of recovering critical materials for energy, defense and manufacturing applications.

Oak Ridge National Laboratory materials scientist Zhili Feng, left, looks on as senior technician Doug Kyle operates a welding robot inside a robotic welding cell. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

The U.S. Departments of Energy and Defense teamed up to create a series of weld filler materials that could dramatically improve high-strength steel repair in vehicles, bridges and pipelines.

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

ORNL’s particle entanglement machine is a precursor to the device that researchers at the University of Oklahoma are building, which will produce entangled quantum particles for quantum sensing to detect underground pipeline leaks. Credit: ORNL, U.S. Dept. of Energy

To minimize potential damage from underground oil and gas leaks, Oak Ridge National Laboratory is co-developing a quantum sensing system to detect pipeline leaks more quickly.

An algorithm developed and field-tested by ORNL researchers uses machine learning to maintain homeowners’ preferred temperatures year-round while minimizing energy costs. Credit: ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.

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.

The CrossVis application includes a parallel coordinates plot (left), a tiled image view (right) and other interactive data views. Credit: Chad Steed/Oak Ridge National Laboratory, U.S. Dept. of Energy

From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.

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.

ORNL-developed cryogenic memory cell circuit designs fabricated onto these small chips by SeeQC, a superconducting technology company, successfully demonstrated read, write and reset memory functions. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

Scientists at have experimentally demonstrated a novel cryogenic, or low temperature, memory cell circuit design based on coupled arrays of Josephson junctions, a technology that may be faster and more energy efficient than existing memory devices.