A world-leading researcher in solid electrolytes and sophisticated electron microscopy methods received Oak Ridge National Laboratory’s top science honor today for her work in developing new materials for batteries. The announcement was made during a livestreamed Director’s Awards event hosted by ORNL Director Thomas Zacharia.
ASM International recently elected three researchers from ORNL as 2021 fellows. Selected were Beth Armstrong and Govindarajan Muralidharan, both from ORNL’s Material Sciences and Technology Division, and Andrew Payzant from the Neutron Scattering Division.
Scientists at ORNL and the University of Tennessee, Knoxville, have found a way to simultaneously increase the strength and ductility of an alloy by introducing tiny precipitates into its matrix and tuning their size and spacing.
The Department of Energy’s Office of Science has selected five Oak Ridge National Laboratory scientists for Early Career Research Program awards.
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.
Scientists have found new, unexpected behaviors when SARS-CoV-2 – the virus that causes COVID-19 – encounters drugs known as inhibitors, which bind to certain components of the virus and block its ability to reproduce.
A multi-institutional team became the first to generate accurate results from materials science simulations on a quantum computer that can be verified with neutron scattering experiments and other practical techniques.
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.
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