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Scientists at the Department of Energy’s Oak Ridge National Laboratory have created a recipe for a renewable 3D printing feedstock that could spur a profitable new use for an intractable biorefinery byproduct: lignin.
Scientists at the Department of Energy’s Oak Ridge National Laboratory used neutrons, isotopes and simulations to “see” the atomic structure of a saturated solution and found evidence supporting one of two competing hypotheses about how ions come
An Oak Ridge National Laboratory-led team used a scanning transmission electron microscope to selectively position single atoms below a crystal’s surface for the first time.
Oak Ridge National Laboratory scientists have improved a mixture of materials used to 3D print permanent magnets with increased density, which could yield longer lasting, better performing magnets for electric motors, sensors and vehicle applications. Building on previous research, ...
The US Department of Energy’s Oak Ridge National Laboratory is once again officially home to the fastest supercomputer in the world, according to the TOP500 List, a semiannual ranking of the world’s fastest computing systems.
The U.S. Department of Energy’s Oak Ridge National Laboratory today unveiled Summit as the world’s most powerful and smartest scientific supercomputer.
Scientists at the Department of Energy’s Oak Ridge National Laboratory are the first to successfully simulate an atomic nucleus using a quantum computer. The results, published in Physical Review Letters, demonstrate the ability of quantum systems to compute nuclear ph...
A scientific team led by the Department of Energy’s Oak Ridge National Laboratory has found a new way to take the local temperature of a material from an area about a billionth of a meter wide, or approximately 100,000 times thinner than a human hair. This discove...
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the