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Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals. Credit: ORNL, U.S. Dept. of Energy

Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography.  Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

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.

Ten scientists from the Department of Energy’s Oak Ridge National Laboratory are among the world’s most highly cited researchers. Credit: ORNL, U.S. Dept. of Energy

Ten scientists from the Department of Energy’s Oak Ridge National Laboratory are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.

Distinguished Inventors

Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.

Coronavirus graphic

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.

Computing—Routing out the bugs

A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool