Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
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.
More than 50 current employees and recent retirees from ORNL received Department of Energy Secretary’s Honor Awards from Secretary Jennifer Granholm in January as part of project teams spanning the national laboratory system. The annual awards recognized 21 teams and three individuals for service and contributions to DOE’s mission and to the benefit of the nation.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
Long-haul tractor trailers, often referred to as “18-wheelers,” transport everything from household goods to supermarket foodstuffs across the United States every year. According to the Bureau of Transportation Statistics, these trucks moved more than 10 billion tons of goods—70.6 ...
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...
Scientists at Oak Ridge National Laboratory have conducted a series of breakthrough experimental and computational studies that cast doubt on a 40-year-old theory describing how polymers in plastic materials behave during processing.
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