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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.
Oak Ridge National Laboratory researchers have developed a thin film, highly conductive solid-state electrolyte made of a polymer and ceramic-based composite for lithium metal batteries.
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.
An international team of researchers has discovered the hydrogen atoms in a metal hydride material are much more tightly spaced than had been predicted for decades — a feature that could possibly facilitate superconductivity at or near room temperature and pressure.
Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
Three researchers at Oak Ridge National Laboratory will lead or participate in collaborative research projects aimed at harnessing the power of quantum mechanics to advance a range of technologies including computing, fiber optics and network
A modern, healthy transportation system is vital to the nation’s economic security and the American standard of living. The U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) is engaged in a broad portfolio of scientific research for improved mobility
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.