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Media Contacts
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.
ORNL researchers are deploying their broad expertise in climate data and modeling to create science-based mitigation strategies for cities stressed by climate change as part of two U.S. Department of Energy Urban Integrated Field Laboratory projects.
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.
Researchers at ORNL are tackling a global water challenge with a unique material designed to target not one, but two toxic, heavy metal pollutants for simultaneous removal.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
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.
For nearly three decades, scientists and engineers across the globe have worked on the Square Kilometre Array (SKA), a project focused on designing and building the world’s largest radio telescope. Although the SKA will collect enormous amounts of precise astronomical data in record time, scientific breakthroughs will only be possible with systems able to efficiently process that data.